Ryszard S. Michalski, Kenneth A. Kaufman, Ryszard S. Michalski, Kenneth A. Kaufman
agents. Project Summary Objectives of this research are to develop, implement, and test a methodology for building inductive databases, which extend conventional databases by integrating inductive...
Chapter 21 Learning as Goal-Driven Inference (2008)
Ryszard S. Michalski, Ashwin Ram
A remarkable aspect of human learners is that they are able to apply a great variety of learning strategies in a flexible and goal-oriented manner and to dynamically accommodate the demands of...
Ryszard S. Michalski, Janusz Wojtusiak
Abstract. This paper concerns the development of a new direction in machine learning, called natural induction, which requires from computergenerated knowledge not only to have high predictive...
Kenneth A. Kaufman, Ryszard S. Michalski, Jaroslaw Pietrzykowski, Janusz Wojtusiak
Abstract. A brief review of the current research on VINLEN multitask inductive database and decision support system is presented. VINLEN integrates a wide range of knowledge generation operators that...
Modeling User Behavior by Integrating AQ Learning with a Database: Initial Results (2008)
Guido Cervone, Ryszard S. Michalski
Abstract: The paper describes recent results from developing and testing LUS methodology for user modeling. LUS employs AQ learning for automatically creating user models from datasets representing...
Janusz Wojtusiak, Ryszard S. Michalski, Thipkesone Simanivanh, Anna V. Baranova
This paper briefly describes the AQ21 learning system that implements a simple form of natural induction, an approach to learning that generates hypotheses in forms resembling natural language...
The Use of Compound Attributes in AQ Learning (2008)
Janusz Wojtusiak, Ryszard S. Michalski
Abstract. Compound attributes are named groups of attributes that have been introduced in Attributional Calculus (AC) to facilitate learning descriptions of objects whose components are characterized...
Discovering Multi-head Attributional Rules in Large Databases (2008)
Cezary Głowi Ski, Ryszard S. Michalski
Abstract: A method for discovering multi-head attributional rules in large databases is presented and illustrated by results from an implemented program. Attributional rules (a.k.a. attributional...
Mark Coletti, Craig Mandsager, Rida Moustafa, Ryszard S. Michalski
This paper describes an application of the Learnable Evolution Model (LEM) to a digital signal filter parameter identification problem, and compares its performance with that of two canonical genetic...
Ryszard S. Michalski, Kenneth A. Kaufman
Given a sufficiently large database, it is usually possible to derive many different hypotheses about the data. Therefore, an important problem in data mining is which hypothesis to select, or, more...
A Rules-to-Trees Conversion in the Inductive Database System VINLEN (2008)
Abstract. Decision trees and rules are completing methods of knowledge representation. Both have advantages in some applications. Algorithms that convert trees to rules are common. In the paper an...
Learning Hybrid Concept Descriptions (2008)
Ryszard S. Michalski, Janusz Wnek
Most symbolic learning methods are concerned with learning concept descriptions in the form of a decision tree or a set of rules expressed in terms of the originally given attributes. For some...
Intelligent Optimization via Learnable Evolution Model (2008)
Ryszard S. Michalski, Janusz Wojtusiak, Kenneth A. Kaufman
A new method for optimizing complex functions and systems is described that employs Learnable Evolution Model (LEM), a form of non-Darwinian evolutionary computation guided by machine learning....
Knowledge Mining: A Proposed New Direction (2008)
In the last several years, the field of data mining has been rapidly expanding, and attracting many new researchers and users. The underlying reason for such a rapid growth is a great need for...
2 Data Mining and Knowledge Discovery: A Review of Issues and a Multistrategy (2008)
Ryszard S. Michalski, Kenneth A. Kaufman, I. Bratko, M. Kubat, John Wiley, ...
An enormous proliferation of databases in almost every area of human endeavor has created a great demand for new, powerful tools for turning data into useful, task-oriented knowledge. In efforts to...
Generating Alternative Hypotheses in AQ Learning (2008)
Ryszard S. Michalski, Ryszard S. Michalski
In many areas of application of machine learning and data mining, it is desirable to generate alternative inductive hypotheses from the given data. The A q-ALT or, briefly, ALT method, presented in...
LEARNING DESIGN RULES FOR WIND BRACINGS IN TALL BUILDINGS (2008)
Tomasz Arciszewski, Associate Member Asce, Eric Bloedorn, Ryszard S. Michalski, Mohamad Mustafa, Janusz Wnek
This paper describes a methodology for applying machine learning to problems of conceptual design, and presents a case study of learning design rules for wind bracings in tall buildings. Design rules...
LEARNING IN AN INCONSISTENT WORLD: (2007)
Kenneth A. Kaufman, Kenneth A. Kaufman, Ryszard S. Michalski, Ryszard S. Michalski
In concept learning and data mining tasks, the learner is typically faced with a choice of many possible hypotheses generalizing the input data. If one can assume that training data contains no...
Ished1: Applying The Lem Methodology To Heat Exchanger Design (2007)
Kenneth A. Kaufman, Kenneth A. Kaufman, Ryszard S. Michalski, Ryszard S. Michalski, Exchanger Design
Evolutionary computation has traditionally employed a Darwinian approach, in which a population of individuals "evolves" based on random perturbations in their "genetic" makeup...
Learning Design Rules For Wind Bracings In Tall Buildings (2007)
Tomasz Arciszewski, Eric Bloedorn, Ryszard S. Michalski, Mohamad Mustafa, Janusz Wnek
This paper describes a methodology for applying machine learning to problems of conceptual design, and presents a case study of learning design rules for wind bracings in tall buildings. Design rules...
PROGRESS IN PATTERN RECOGNITION (2007)
Ryszard Michalki, Laveen N. Kana, Azriel Rosenfeld, Ln. Kanal, Ryszard S. Michalski, Edwin Diday, ...
by
Modeling User Behavior by Integrating AQ Learning with a Database: Initial Results (2007)
Guido Cervone, Ryszard S. Michalski
Abstract: The paper describes recent results from developing and testing LUS methodology for user modeling. LUS employs AQ learning for automatically creating user models from datasets representing...
2 ISHED1: APPLYING THE LEM METHODOLOGY TO HEAT (2007)
Kenneth A. Kaufman, Kenneth A. Kaufman, Ryszard S. Michalski, Ryszard S. Michalski, Exchanger Design
Evolutionary computation has traditionally employed a Darwinian approach, in which a population of individuals “evolves ” based on random perturbations in their “genetic ” makeup and the...
A General Description and User’s Guide Summary (2007)
Ryszard S. Michalski, Ryszard S. Michalski, Kenneth A. Kaufman, Kenneth A. Kaufman
This report provides a description and a user’s guide for AQ19, a program for machine learning and pattern discovery. AQ19 works in two modes: Theory Formation and Pattern Discovery. In Theory...
American Association for Artificial Intelligence Multistrategy Task-adaptive Learning Using (2007)
Edited Ryszard, S. Michalski, Janusz Wnek, Nabil W. Alkharouf, Ryszard S. Michalski
This research concerns the development of a methodology for representing, plazaflag and executing multitype inferences in a multistrategy task-adaptive learning system. These inferences, defined in...
Conceptual Clustering Versus Numerical Taxonomy (2007)
Abstract-A method for automated construction of classifications called conceptual clustering is described and compared to methods used in numerical taxonomy. This method arranges objects into classes...
LEARNING STRATEGIES AND AUTOMATED KNOWLED GE ACQUISITION: (2007)
Ryszard Michal, An Overview, Ryszard Michalskl, Ryszard S. Michalski
by
THE INLEN SYSTEM FOR EXTRACTING KNOWLEDGE FROM DATABASES: Goals and General Description (2007)
K. Kaufman, R. S. Michalski, J. Zytkow, L. Kerschberg, Kenneth A. Kaufman, Ryszard S. Michalski, ...
The INLEN system combines database, knowledge base, and machine learning technologies to provide a user with an integrated system of tools for conceptually analyzing data and searching for...
Machine Learmug: A Historical and MethodOlogical Analysis (2007)
J. G. Carbonell, R. S. Michalski, T. M. Mitchell, Jaime G. Carbonell, Ryszard S. Michalski, Tom M. Mitchell
Editors ' Note: Machine Learning has been a constant theme throughout Al's two decades o [ existence. In this overa ew the authors analyze various aspects including the major.thodological...
and R. S. Michalski (Eds.), Kluwer Publishing Company, 1986. (2007)
R. S. Michalski, Ryszard S. Michalski
This note provides a brief account of major projects on Machine Learning done in
MACHINE LEARNING An Artificial Intelligence Approach Contributing authors: (2007)
John Anderson, Ranan Banerji, Gary Bradshaw, Jaime Carbonell, Thomas Dietterich, Norman Haas, ...
by
SYMBOLIC COMPUTATION- Artificial Intelligence (2007)
Ryszard S. Michalski, Leonard Bolc (ed, G. L. Bradshaw, P. Langley, R. S. Michalski, S. Ohlsson, ...
139 figs., 1982
Descriptions from Examples (2007)
R. S. Michalski, Ryszad S. Michalski, William A. Hoff, William A. Hoff, Ryszard S. Michalski
All rights to this publication belong to the author/s. Permission is required for duplication by the University of Illinois, Department of Computer Science, Artificial
RECOGNITION OF TCITAL OR PARTIAL SYMMETRY IN A COMPLETELY OR INCOMPLETELY (2007)
Ryszard S. Michalski, Naczelna Organizacia, Techniczna Polsce, Specipied Switching Punction
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1 UNDERSTANDING THE NATURE OF LEARNING: Issues and Research Directions (2007)
This chapter presents an overview of goals and directions in machine learning research and serves as a conceptual road map to other chapters. [t investigates intrinsic aspects of the learning...
A GENERAL CRITERION FOR MEASURING (2007)
F. Bergadano, S. Matwin, R. S. Michalski, J. Zhang, Francesco Bergadano, Stan Matwin, ...
An important aspect of any learning method is an evaluation of the learned knowledge, in particular, an evaluation of the plausibility and usefulness of concept descriptions that are being created....
J. Wnek, R. S. Michalski, Janusz Wnek, Janusz Wnek, Ryszard S. Michalski, Ryszard S. Michalski
The paper discusses and experimentally compares five different methods for concept learning from examples. The first three are symbolic methods, specifically, a decision tree learning method (C4.5),...
Agricultural Advisory Systems (2007)
T. Fermanian, R. Michalski, Charles K. Mann, Stephen R. Ruth, Thomas W. Fermanian, Ryszard S. Michalski
by
The Two-tiered Concept Representation (2007)
Ryszard S. Michalski, Ryszard S. Michalski, I. Van Mechelen, R. S. Michalsld
by
INTEGRATING QUANTITATIVE AND QUALITATIVE DISCOVERY IN THE (2007)
Abacus System, B. C. Falkenhainer, R. S. Michalski, Brian C. Falkenhaincr, Ryszard S. Michalski
Most research on inductive learning has been concerned with qualitative learn-ing that creates conceptual, logic-style descriptions from the given facts. In contrast, quantitative learning deals with...
PROGRAMMER'S GUIDE FOR THE SUN WORKSTATION (2007)
K. Kaufman, R. S. Michalski, Kenneth A. Kaufman, Kenneth A, Alan C. Schultz, Alan C. Schultz, ...
EMERALD 1 is a large-scale system integrating several advanced programs exhibiting different forms of learning or discovery. The system is intended to support teaching and research in the area of...
INFERENTIAL LEARNING THEORY: A Conceptual Framework for Characterizing Learning Processes (2007)
Ryszard S. Michaiski, Ryszard S. Michalski, Ryszard S. Michalski
The paper presents initial results toward developing a unifying conceptual framework for characterizing diverse learning strategies and paradigms. It outlines the Inferential Learning Theory (ILT)...
INPUT UNDERSTANDING AS A BASIS FOR (2007)
R. S. Michalski, G. Tecuci, Gheorghe Tecuci, Ryszard S. Michalski
The paper explores several general issues in developing a multistrategy task-adaptive learning (MTL) system. The system aims at integrating a whole range of learning strategies, such as...
K. Kaufman, R. S. Michalski, L. Kerschberg, Kenneth A. Kaufman, Kenneth A. Kaufman, Ryszard S. Michalski, ...
The architecture of a large-scale system, INLEN, for the discovery of knowledge from facts, is described and then illustrated by an exploratory application. INLEN combines database, knowledge base,...
Categories and Concepts Theoretical Views and Inductive Data Analysis (2007)
Van Mechelen, I. Hampton, R. S. Michaiski, P. Theuns, Iven Van Mechelen, James Hampton, ...
by
COMPARING SYMBOLIC AND SUBSYMBOLIC LEARNING: Three Studies (2007)
G. Tecuci, Morgan Kaumann, Janusz Wnek, Ryszard S. Michalski
This chapter reports on three studies comparing symbolic and subsymbolic methods for concept learning from examples. The first study compared five learning methods, three represenling symbolic...
J. Wnek, R. S. Michalski, Janusz Wnek, Ryszard S. Michalski
ABSTRACT--This paper reports on three studies comparing symbolic and subsymbolic methods for concept learning from examples. The first study compared five learning methods, three representing...
R. S. Michalski, P. W. Pachowicz, A. Rosenfeld, Y. Aloimonos, Ryszard S. Michalski, Peter W. Pachowicz, ...
This report gives a brief account of the
Abstract. The paper presents initial results from an emerging new direction in engineering design research, in particular, creative design. It argues that constructive induction, which was originally...
PROGRAMMER'S GUIDE FOR THE DEC VAXSTATION (2007)
K. Kaufman, R. S. Michalski, Kenneth A. Kaufman, Kenneth A. Kaufman, Ryszard S. Michalski, Ryszard S. Michalski
EMERALD 1 is a large-scale system integrau.'ng several advanced programs exhibiting different forms of learning or discovery. The system is rotended to support teaching and research in the area...
Learning Hybrid Concept Descriptions (2007)
Ryszard S. Michalski, Janusz Wnek
Most symbolic learning methods are concerned with learning concept descriptions in the form of a decision tree or a set of rules expressed in terms of the originally given attributes. For some...
Ryszard S. Michalski, Kenneth A. Kaufman
Given a sufficiently large database, it is usually possible to derive many different hypotheses about the data. Therefore, an important problem in data mining is which hypothesis to select, or, more...
pp. 70--79 AQ-PM: A System for Partial Memory Learning (2007)
Marcus A. Maloof, Ryszard S. Michalski
Abstract. This paper describes AQ-PM, a system for partial memory learning, which determines and memorizes representative concept examples, and then uses them with new training examples to induce new...
Semantic and Syntactic Attribute Types in AQ Learning (2007)
Michalski, Ryszard S., Wojtusiak, Janusz
AQ learning strives to perform natural induction that aims at deriving general descriptions from specific data and formulating them in human-oriented forms. Such descriptions are in the forms closely...
Progress Report on the Learnable Evolution Model (2007)
Michalski, Ryszard S., Wojtusiak, Janusz, Kaufman, Kenneth
This report reviews recent research on Learnable Evolution Model (LEM), and presents selected results from its application to the optimization of complex functions and engineering designs. Among the...
PROGRESS REPORT ON LEARNABLE EVOLUTION MODEL (2007)
Ryszard S. Michalski, Janusz Wojtusiak, Kenneth A. Kaufman, Ryszard S. Michalski, Janusz Wojtusiak, Kenneth A. Kaufman
This report reviews recent research on Learnable Evolution Model (LEM), and presents selected results from its application to the optimization of complex functions and engineering designs. Among the...
Janusz Wojtusiak, Janusz Wojtusiak, Co-director James, E. Gentle, To Professor, Ryszard S. Michalski
iii
Wojtusiak, Janusz, Michalski, Ryszard S.
© ACM, 2006. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution.
Wojtusiak, Janusz, Michalski, Ryszard S., Kaufman, Kenneth A., Pietrzykowski, Jaroslaw
The AQ21 program seeks different types of patterns in data and represents them in human-oriented forms resembling natural language descriptions. Because of the latter feature it is called a natural...
Natural Induction and Conceptual Clustering: A Review of Applications (2006)
Michalski, Ryszard S., Kaufman, Kenneth A., Pietrzykowski, Jaroslaw, Wojtusiak, Janusz, Mitchell, Scott, Seeman, Doug
Natural induction and conceptual clustering are two methodologies pioneered by the GMU Machine Learning and Inference Laboratory for discovering conceptual relationships in data, and presenting them...
The Use of Compound Attributes in AQ Learning (2006)
Wojtusiak, Janusz, Michalski, Ryszard S.
Compound attributes are named groups of attributes that have been introduced in Attributional Calculus (AC) to facilitate learning descriptions of objects whose components are characterized by...
Learning Symbolic User Models for Intrusion Detection: A Method and Initial Results (2006)
Michalski, Ryszard S., Kaufman, Kenneth A., Pietrzykowski, Jaroslaw, Śnieżyński, Bartłomiej, Wojtusiak, Janusz
This paper briefly describes the LUS-MT method for automatically learning user signatures (models of computer users) from datastreams capturing users’ interactions with computers. The signatures...
Michalski, Ryszard S., Tecuci, Gheorghe
This volume contains the papers accepted for presentation at the Second International Workshop on Multistrategy Learning (briefly, MSL-93), held in Harpers Ferry, WV, May 26-29, 1993. The workshop...
Janusz Wojtusiak, Ryszard S. Michalski, Kenneth A. Kaufman, Jaroslaw Pietrzykowski, Janusz Wojtusiak, Ryszard S. Michalski, ...
The AQ21 program seeks different types of patterns in data and represents them in human-oriented forms resembling natural language descriptions. Because of the latter feature it is called a natural...
Natural Induction and Conceptual Clustering: A Review of Applications (2006)
Ryszard S. Michalski, Kenneth A. Kaufman, Jaroslaw Pietrzykowski, Janusz Wojtusiak, Scott Mitchell, Doug Seeman, ...
Natural induction and conceptual clustering are two methodologies pioneered by the GMU Machine Learning and Inference Laboratory for discovering conceptual relationships in data, and presenting them...
Learning Symbolic User Models for Intrusion Detection: A Method and (2006)
Ryszard S. Michalski, Kenneth A. Kaufman, Jaroslaw Pietrzykowski, Bartlomiej Sniezynski, Janusz Wojtusiak
Abstract. This paper briefly describes the LUS-MT method for automatically learning user signatures (models of computer users) from datastreams capturing users ’ interactions with computers. The...
Janusz Wojtusiak, Ryszard S. Michalski
ABSTRACT 1 Learnable Evolution Model (LEM) is a form of non-Darwinian evolutionary computation that employs machine learning to guide evolutionary processes. Its main novelty are new type of...
George Mason University NATURAL INDUCTION AND CONCEPTUAL CLUSTERING: A REVIEW OF APPLICATIONS (2006)
Ryszard S. Michalski, Kenneth A. Kaufman, Jaroslaw Pietrzykowski, Janusz Wojtusiak, Scott Mitchell, Doug Seeman, ...
Natural induction and conceptual clustering are two methodologies pioneered by the GMU Machine Learning and Inference Laboratory for discovering conceptual relationships in data, and presenting them...
Janusz Wojtusiak, Janusz Wojtusiak, Ryszard S. Michalski, Ryszard S. Michalski, Kenneth A. Kaufman, Kenneth A. Kaufman, ...
The AQ21 program seeks different types of patterns in data and represents them in human-oriented forms resembling natural language descriptions. Because of the latter feature it is called a natural...
Michalski, Ryszard S., Kaufman, Kenneth A., Pietrzykowski, Jaroslaw, Śnieżyński, Bartłomiej, Wojtusiak, Janusz
This paper presents a description of the LUS method for creating models (signatures) of computer users from datastreams that characterize users' interactions with computers, and the results of...
Wojtusiak, Janusz, Michalski, Ryszard S.
LEM3 is the newest implementation of Learnable Evolution Model (LEM), a non-Darwinian evolutionary computation methodology that employs machine learning to guide evolutionary processes. Due to a deep...
Reasoning with Meta-values in AQ Learning (2005)
Michalski, Ryszard S., Wojtusiak, Janusz
This paper describes methods for reasoning with missing, irrelevant and not applicable meta-values in the AQ attributional rule learning. The methods address issues of handling these values in...
Knowledge Visualization Using Optimized General Logic Diagrams (2005)
Śnieżyński, Bartłomiej, Szymacha, Robert, Michalski, Ryszard S.
Knowledge Visualizer (KV) uses a General Logic Diagram (GLD) to display examples and/or various forms of knowledge learned from them in a planar model of a multi-dimensional discrete space. Knowledge...
A Rules-to-Trees Conversion in the Inductive Database System VINLEN (2005)
Śnieżyński, Bartłomiej, Michalski, Ryszard S.
Decision trees and rules are completing methods of knowledge representation. Both have advantages in some applications. Algorithms that convert trees to rules are common. In the paper an algorithm...
From Data Mining to Knowledge Mining (2005)
Kaufman, Kenneth A., Michalski, Ryszard S.
In view of the tremendous production of computer data worldwide, there is a strong need for new powerful tools that can automatically generate useful knowledge from a variety of data, and present it...
REASONING WITH MISSING, NOT-APPLICABLE AND IRRELEVANT (2005)
Ryszard S. Michalski, Janusz Wojtusiak, S. Michalski, Janusz Wojtusiak, Ryszard S. Michalski
Title: Reasoning with missing, not-applicable and irrelevant meta-values
Janusz Wojtusiak, Janusz Wojtusiak, Ryszard S. Michalski, Ryszard S. Michalski
LEM3 is the newest implementation of Learnable Evolution Model (LEM), a non-Darwinian evolutionary computation methodology that employs machine learning to guide evolutionary processes. Due to a deep...
Ryszard S. Michalski, Ryszard S. Michalski, Kenneth A. Kaufman, Kenneth A. Kaufman, Jaroslaw Pietrzykowski, Jaroslaw Pietrzykowski, ...
This paper presents a description of the LUS method for creating models (signatures) of computer users from datastreams that characterize users ' interactions with computers, and the results of...
Knowledge Visualization Using Optimized General Logic Diagrams (2005)
Robert Szymacha, Ryszard S. Michalski
Abstract. Knowledge Visualizer (KV) uses a General Logic Diagram (GLD) to display examples and/or various forms of knowledge learned from them in a planar model of a multi-dimensional discrete space....
Reasoning with Meta-values in AQ Learning (2005)
Ryszard S. Michalski, Ryszard S. Michalski, Janusz Wojtusiak, Janusz Wojtusiak
This paper describes methods for reasoning with missing, irrelevant and not applicable meta-values in the AQ attributional rule learning. The methods address issues of handling these values in...
Generating Alternative Hypotheses in AQ Learning (2004)
In many areas of application of machine learning and data mining, it is desirable to generate alternative inductive hypotheses from the given data. The Aq-ALT or, briefly, ALT method, presented in...
Initial Considerations toward Knowledge Mining (2004)
Kaufman, Kenneth A., Michalski, Ryszard S.
In view of the tremendous production of computer data worldwide, there is a strong need for new powerful tools that can automatically generate useful knowledge from a variety of data, and present it...
Attributional Calculus: A Logic and Representation Language for Natural Induction (2004)
Attributional calculus (AC) is a typed logic system that combines elements of propositional logic, predicate calculus, and multiple-valued logic for the purpose of natural induction. By natural...
An Optimized Design of Finned-Tube Evaporators Using the Learnable Evolution Model (2004)
Domanski, Piotr A., Yashar, David, Kaufman, Kenneth A., Michalski, Ryszard S.
Optimizing the refrigerant circuitry for a finned-tube evaporator is a daunting task for traditional exhaustive search techniques due to the extremely large number of circuitry possibilities. For...
Attributional Calculus: A Logic and Representation Language for Natural Induction (2004)
Ryszard S. Michalski, Ryszard S. Michalski
Attributional calculus ( ¢ ¡ ) is a typed logic system that combines elements of propositional logic, predicate calculus, and multiple-valued logic for the purpose of natural induction. By...
An Optimized Design of Finned-Tube Evaporators Using the Learnable Evolution Model (2004)
Piotr A. Domanski, Piotr A. Domanski, David Yashar, David Yashar, Kenneth A. Kaufman, Kenneth A. Kaufman, ...
Optimizing the refrigerant circuitry for a finned-tube evaporator is a daunting task for traditional exhaustive search techniques due to the extremely large number of circuitry possibilities. For...
INITIAL CONSIDERATIONS TOWARD KNOWLEDGE MINING (2004)
Kenneth A. Kaufman, Ryszard S. Michalski, Kenneth A. Kaufman, Ryszard S. Michalski
In view of the tremendous production of computer data worldwide, there is a strong need for new powerful tools that can automatically generate useful knowledge from a variety of data, and present it...
The Development of the Inductive Database System VINLEN: A Review of Current Research (2003)
Cervone, Guido, Kaufman, Kenneth A., Michalski, Ryszard S.
The recently introduced Learnable Evolution Model (LEM) represents a form of non-Darwinian evolutionary computation that is guided by a learning system. Specifically, LEM "genetically engineers" new...
Kaufman, Kenneth A., Cervone, Guido, Michalski, Ryszard S.
This paper describes briefly a method for applying AQ symbolic learning to problems of computer user modeling and intrusion detection. The method, called LUS (Learning User Signatures), learns models...
Inferential Theory of Learning and Inductive Databases (2003)
This research was performed at the Machine Learning and Inference Laboratory at George Mason University. Laboratory's research activities are supported in part by the National Science Foundation...
Knowledge Mining: A Proposed New Direction (2003)
This research was performed at the Machine Learning and Inference Laboratory at George Mason University. Laboratory's research activities are supported in part by the National Science Foundation...
The Development of the Inductive Database System VINLEN: A Review of Current Research (2003)
Kaufman, Kenneth A., Michalski, Ryszard S.
Current research on the VINLEN inductive database system is briefly reviewed and illustrated by selected results. The goal of research on VINLEN is to develop a methodology for deeply integrating a...
Michalski, Ryszard S., Wnek, Janusz
The Third International Workshop on Multistrategy Learning (MSL-96), held in Harpers Ferry, WV, May 23-25, 1996, attracted leading researchers in this area from Australia, Austria, Belgium, France,...
The Development of the Inductive Database System VINLEN: A Review of (2003)
Kenneth A. Kaufman, Ryszard S. Michalski
Abstract. Current research on the VINLEN inductive database system is briefly reviewed and illustrated by selected results. The goal of research on VINLEN is to develop a methodology for deeply...
Validating Learnable Evolution Model on Selected Optimization and Design Problems (2003)
Guido Cervone, Guido Cervone, Kenneth A. Kaufman, Kenneth A. Kaufman, Ryszard S. Michalski, Ryszard S. Michalski
The recently introduced Learnable Evolution Model (LEM) represents a form of non-Darwinian evolutionary computation that is guided by a learning system. Specifically, LEM “genetically engineers ”...
Kenneth A. Kaufman, Kenneth A. Kaufman, Guido Cervone, Guido Cervone, Ryszard S. Michalski, Ryszard S. Michalski
This paper describes briefly a method for applying AQ symbolic learning to problems of computer user modeling and intrusion detection. The method, called LUS (Learning User Signatures), learns models...
Validating Learnable Evolution Model on Selected Optimization and Design Problems (2003)
Guido Cervone, Guido Cervone, Kenneth A. Kaufman, Kenneth A. Kaufman, Ryszard S. Michalski, Ryszard S. Michalski
The recently introduced Learnable Evolution Model (LEM) represents a form of non-Darwinian evolutionary computation that is guided by a learning system. Specifically, LEM “genetically engineers ”...
Attributional Ruletrees: A New Representation for AQ Learning (2002)
Attributional ruletrees are proposed as an extension of the current ruleset representation used by AQ type learning. The ruletrees split a multiclass classification problem into separate subproblems...
Recent Results from the Experimental Evaluation of the Learnable Evolution Model (2002)
Cervone, Guido, Kaufman, Kenneth A., Michalski, Ryszard S.
The Learnable Evolution Model (LEM) represents a form of non-Darwinian evolutionary computation that is guided by a learning system. Specifically, LEM "genetically engineers" new populations via...
Modeling User Behavior by Integrating AQ Learning with a Database: Initial Results (2002)
Cervone, Guido, Michalski, Ryszard S.
The paper describes recent results from developing and testing LUS methodology for user modeling. LUS employs AQ learning for automatically creating user models from datasets representing activities...
Mitchell,Tom M., Carbonell,Jaime G., Michalski,Ryszard S.
The Third International Machine Learning Workshop brought together approximately 100 researchers working on computer programs that learn. The workshop covered topics such as inductive generalization,...
Attributional Ruletrees: A New Representation for AQ (2002)
Ryszard S. Michalski, Ryszard S. Michalski
Attributional ruletrees are proposed as an extension of the current ruleset representation used by AQ type learning. The ruletrees split a multiclass classification problem into separate subproblems...
Discovering Multi-head Attributional Rules in Large Databases (2001)
Głowiński, Cezary, Michalski, Ryszard S.
A method for discovering multi-head attributional rules in large databases is presented and illustrated by results from an implemented program. Attributional rules (a.k.a. attributional dependencies)...
Adaptive Anchoring Discretization for Learnable Evolution Model: The ANCHOR Method (2001)
Michalski, Ryszard S., Cervone, Guido
To apply a symbolic learning method to learning in a continuous representation space, the variables spanning the space need to be discretized. When the space is very large, a problem arises as to how...
Michalski, Ryszard S., Kaufman, Kenneth A.
Research on the development of the AQ methodology and AQ programs has been conducted in the Machine Learning and Inference Laboratory at George Mason University and previously at the Artificial...
The Development of the AQ20 Learning System and Initial Experiments (2001)
Michalski, Ryszard S., Cervone, Guido, Panait, Liviu A.
Research on a new system implementing the AQ learning methodology, called AQ20, is briefly described, and illustrated by initial results from an experimental version. Like its predecessors, AQ20 is a...
Learning Patterns in Noisy Data: The AQ Approach (2001)
Michalski, Ryszard S., Kaufman, Kenneth A.
This research was conducted in the Machine Learning and Inference Laboratory at George Mason University. The Laboratory's research activities have been supported in part by the National Science...
Learning Patterns in Noisy Data: the AQ Approach (2001)
Ryszard S. Michalski, Kenneth A. Kaufman
In concept learning and data mining, a typical objective is to determine concept descriptions or patterns that will classify future data points as correctly as possible. If one can assume that the...
Ryzsard S. Michalski, Ryszard S. Michalski, Kenneth A. Kaufman, Kenneth A. Kaufman
This report provides a description and a user’s guide for AQ19, a program for machine learning and pattern discovery. AQ19 works in two modes: Theory Formation and Pattern Discovery. In Theory...
Adaptive Anchoring Discretization for Learnable Evolution Model (2001)
Ryzsard S. Michalski, Ryszard S. Michalski, Guido Cervone, Guido Cervone
To apply a symbolic learning method to learning in a continuous representation space, the variables spanning the space need to be discretized. When the space is very large, a problem arises as to how...
A Knowledge Scout for Discovering Medical Patterns: Methodology and System SCAMP (2000)
Kaufman, Kenneth A., Michalski, Ryszard S.
Knowledge scouts are software agents that autonomously synthesize knowledge of interest to a given user (target knowledge) by applying inductive database operators to a local or distributed dataset....
Experimental Validations of the Learnable Evolution Model (2000)
Cervone, Guido, Kaufman, Kenneth A., Michalski, Ryszard S.
A recently developed approach to evolutionary computation, called Learnable Evolution Model or LEM, employs machine learning to guide processes of generating new populations. The central new idea of...
Combining Machine Learning with Evolutionary Computation: Recent Results on LEM (2000)
Cervone, Guido, Michalski, Ryszard S., Kaufman, Kenneth A., Panait, Liviu A.
The Learnable Evolution Model (LEM), first presented at the Fourth International Workshop on Multistrategy Learning, employs machine learing to guide evolutionary computation. Specifically, LEM...
Speeding Up Evolution through Learning: LEM (2000)
Michalski, Ryszard S., Cervone, Guido, Kaufman, Kenneth A.
This paper reports briefly on the development of a new approach to evolutionary computation, called the Learnable Evolution Model or LEM. In contrast to conventional Darwinian-type evolutionary...
Michalski, Ryszard S., Kaufman, Kenneth A.
This report is a comprehensive user's guide for AQ18, an environment for natural induction, machine learning and knowledge discovery. By natural induction is meant a form of inductive inference which...
Kaufman, Kenneth A., Michalski, Ryszard S.
In concept learning and data mining tasks, the learner is typically faced with a choice of many possible hypotheses or patterns characterizing the input data. If one can assume that training data...
ISHED1: Applying the LEM Methodology to Heat Exchanger Design (2000)
Michalski, Ryszard S., Kaufman, Kenneth A.
The authors thank the National Institute of Standards and Technology and Intelligent Information Systems, Inc. for their support on this project. This research was conducted in the Machine Learning...
Building Knowledge Scouts Using KGL Metalanguage (2000)
Michalski, Ryszard S., Kaufman, Kenneth A.
Knowledge scouts are software agents that autonomously search for and synthesize user-oriented knowledge (target knowledge) in large local or distributed databases. A knowledge generation...
Applying Learnable Evolution Model to Heat Exchanger Design (2000)
Kaufman, Kenneth A., Michalski, Ryszard S.
This article copyright © 2000 by the
Learning and Evolution: An Introduction to Non-Darwinian Evolutionary Computation (2000)
The field of evolutionary computation has drawn inspiration from Darwinian evolution in which species adapt to the environment through random variations and selection of the fittest. This type of...
Combining machine learning with evolutionary computation: Recent results on lem (2000)
Guido Cervone, Ryszard S. Michalski, Kenneth K. Kaufman, Liviu A. Panait
The Learnable Evolution Model (LEM), first presented at the Fourth International Workshop on Multistrategy Learning, employs machine learing to guide evolutionary computation. Specifically, LEM...
Building Knowledge Scouts Using KGL Metalanguage (2000)
Ryszard S. Michalski, Kenneth A. Kaufman
Knowledge scouts are software agents that autonomously search for and synthesize user-oriented knowledge (target knowledge) in large local or distributed databases. A knowledge generation...
Kenneth Kaufman, Ryszard S. Michalski
This report is a comprehensive user’s guide for AQ18, an environment for natural induction, machine learning and knowledge discovery. By natural induction is meant a form of inductive inference...
Learnable evolution model: Evolutionary processes guided by machine learning (2000)
Ryszard S. Michalski, Floriana Esposito, Lorenza Saitta
Abstract. A new class of evolutionary computation processes is presented, called Learnable Evolution Model or LEM. In contrast to Darwinian-type evolution that relies on mutation, recombination, and...
Experimental validations of the learnable evolution model (2000)
Guido Cervone, Kenneth K. Kaufman, Ryszard S. Michalski
A recently developed approach to evolutionary computation, called Learnable Evolution Model or LEM, employs machine learning to guide processes of generating new populations. The central new idea of...
A Knowledge Scout for Discovering Medical Patterns: Methodology and System (2000)
Kenneth A. Kaufman, Ryszard S. Michalski
Abstract. Knowledge scouts are software agents that autonomously synthesize knowledge of interest to a given user (target knowledge) by applying inductive database operators to a local or distributed...
Combining machine learning with evolutionary computation: Recent results on lem (2000)
Guido Cervone, Ryszard S. Michalski, Kenneth K. Kaufman, Liviu A. Panait
The Learnable Evolution Model (LEM), first presented at the Fourth International Workshop on Multistrategy Learning, employs machine learing to guide evolutionary computation. Specifically, LEM...
Applying Learnable Evolution Model to Heat Exchanger Design (2000)
Kenneth A. Kaufman, Ryszard S. Michalski
A new approach to evolutionary computation, called Learnable Evolution Model (LEM), has been applied to the problem of optimizing tube structures of heat exchangers. In contrast to conventional...
Michalski, Ryszard S., Zhang, Qi
Slightly updated version of report MLI 98-3.
Ryszard S. Michalski, Ryszard S. Michalski, Qi Zhang, Qi Zhang
This report presents results of a series of experiments on applying LEM1, a preliminary implementation of Learnable Evolution Model, to a sample of problems in function optimization and evolvable...
M. Coletti, T. Lash, R. S. Michalski, Mark Coletti, Tom Lash, Craig M, ...
This paper describes an application of LEM1, a preliminary implementation of Learnable Evolution Model (LEM), and two evolutionary algorithms, GA1 and GA2, to parameter estimation in non-linear...
Learning in an Inconsistent World: Rule Selection in STAR/AQ18 (1999)
Kenneth A. Kaufman, Kenneth A. Kaufman, Ryszard S. Michalski, Ryszard S. Michalski
In concept learning and data mining tasks, the learner is typically faced with a choice of many possible hypotheses generalizing the input data. If one can assume that training data contains no...
M. Coletti, T. Lash, R. S. Michalski, R. Moustafa, Mark Coletti, Tom Lash, ...
This paper describes an application of LEM1, a preliminary implementation of Learnable Evolution Model (LEM), and two canonical genetic algorithms, GA1 and GA2, to parameter estimation in digital...
LEARNING IN AN INCONSISTENT WORLD: (1999)
Kenneth A. Kaufman, Ryszard S. Michalski, Kenneth A. Kaufman, Ryszard S. Michalski, Rule Selection
In concept learning and data mining tasks, the learner is typically faced with a choice of many possible hypotheses generalizing the input data. If one can assume that training data contains no...
Learning from Inconsistent and Noisy Data: The AQ18 Approach (1999)
Kenneth A. Kaufman, Ryszard S. Michalski
Abstract. In concept learning or data mining tasks, the learner is typically faced with a choice of many possible hypotheses characterizing the data. If one can assume that the training data are...
DISCOVERING MULTIDIMENSIONAL PATTERNS IN LARGE DATASETS USING KNOWLEDGE SCOUTS (1999)
Ryszard S. Michalski, Kenneth A. Kaufman, Ryszard S. Michalski, Kenneth A. Kaufman
This paper presents the concept of a knowledge scout, an intelligent agent that operates within an inductive database to automatically search for target knowledge. A knowledge scout is defined by a...
Machine Learning. Part 1. A Historical and Methodological Analysis. (1998)
Carbonell,Jaime G., Michalski,Ryszard S., Mitchell,Tom M.
Machine learning has always been an integral part of artificial intelligence, and its methodology has evolved in concert with the major concerns of the field. In response to the difficulties of...
DATA-DRIVEN CONSTRUCTIVE INDUCTION: A Methodology and Experiments (1998)
Eric Bloedorn, Ryszard S. Michalski
The presented methodology concerns constructive induction, viewed generally as a process combining two intertwined searches: first for the “best ” representation space, and second for the “best...
Discovery Planning: Multistrategy Learning in Data Mining (1998)
Kenneth A. Kaufman, Ryszard S. Michalski
The process of applying machine learning to data mining may require many trials, backtracking, and multiple executions of different learning and inference procedures. Such a process can be...
DATA-DRIVEN CONSTRUCTIVE INDUCTION: A Methodology and Its Applications (1998)
Eric Bloedorn, Ryszard S. Michalski
The presented methodology concerns constructive induction, viewed generally as a process combining two intertwined searches: first for the "best" representation space, and second for the...
Discovery Planning: Multistrategy Learning in Data Mining (1998)
Kenneth A. Kaufman, Ryszard S. Michalski
The process of applying machine learning to data mining may require many trials, backtracking, and multiple executions of different learning and inference procedures. Such a process can be...
Data Mining and Knowledge Discovery: A Review of Issues and a Multistrategy Approach (1998)
Ryszard S. Michalski, Ryszard S. Michalski, Kenneth A. Kaufman, Kenneth A. Kaufman
An enormous proliferation of databases in almost every area of human endeavor has created a great demand for new, powerful tools for turning data into useful, task-oriented knowledge. In efforts to...
Computer Vision through Learning (1997)
Maloof, Marcus A., Rosenfeld, Azriel, Duric, Zoran, Aloimonos, Yiannis, Zhang, Qi, Michalski, Ryszard S.
KGL: A Language for Learning (1997)
Kenneth A. Kaufman, Kenneth A. Kaufman, Ryszard S. Michalski, Ryszard S. Michalski
In real-life data mining endeavors, the extraction of important knowledge may require many trials and errors, and multiple executions of different sequences of data mining operations. Such...
Learning Symbolic Descriptions Of Shape For Object Recognition In X-Ray Images (1997)
Marcus A. Maloof, Ryszard S. Michalski, S. Michalski
In this paper, we describe a method for learning shape descriptions of objects in x-ray images. The descriptions are induced from shape examples using the AQ15c inductive learning system. The method...
Multistrategy Data Exploration Using the INLEN System: Recent Advances (1997)
Ryszard S. Michalski, Kenneth A. Kaufman
Recent advances in the development of the INLEN system for multistrategy data exploration are briefly reviewed. These advances include the development of a meta-level language for data mining and...
Data Mining and Knowledge Discovery: A Review of Issues and a Multistrategy Approach (1997)
I. Bratko, M. Kubat, John Wiley, Ryszard S. Michalski, Kenneth A. Kaufman
An enormous proliferation of databases in almost every area of human endeavor has created a great demand for new, powerful tools for turning data into useful, task-oriented knowledge. In efforts to...
Data Mining and Knowledge Discovery: A Review of Issues and a Multistrategy Approach (1997)
Ryszard S. Michalski, Ryszard S. Michalski, Kenneth A. Kaufman, Kenneth A. Kaufman
An enormous proliferation of databases in almost every area of human endeavor has created a great demand for new, powerful tools for turning data into useful, task-oriented knowledge. In efforts to...
KGL: A Language for Learning (1997)
Kenneth A. Kaufman, Kenneth A. Kaufman, Ryszard S. Michalski, Ryszard S. Michalski
In real-life data mining endeavors, the extraction of important knowledge may require many trials and errors, and multiple executions of different sequences of data mining operations. Such...
Multistrategy Data Exploration Using the INLEN System: Recent Advances (1997)
Ryszard S. Michalski, Kenneth A. Kaufman
Recent advances in the development of the INLEN system for multistrategy data exploration are briefly reviewed. These advances include the development of a meta-level language for data mining and...
An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules (1996)
Kenneth A. Kaufman, Ryszard S. Michalski
Structured attributes have domains (value sets) that are partially ordered sets, typically hierarchies. Such attributes allow knowledge discovery programs to incorporate background knowledge about...
Eric Bloedorn, Ryszard S. Michalski
. Constructive induction divides the problem of learning an inductive hypothesis into two intertwined searches: one---for the "best" representation space, and two---for the "best"...
A Multistrategy Conceptual Analysis of Economic Data (1996)
Ryszard S. Michalski, Kenneth A. Kaufman, Kenneth A. Kaufman
The goal of the multistrategy tool, INLEN, is to serve as an intelligent assistant for discovering knowledge in large databases. INLEN has been applied to, and is well-suited for the exploration of...
Eric Bloedorn, Ryszard S. Michalski
Abstract. Constructive induction divides the problem of learning an inductive hypothesis into two intertwined searches: one—for the “best ” representation space, and two—for the “best ”...
Kenneth A. Kaufman, Ryszard S. Michalski
Structured attributes have domains (value sets) that are partially ordered sets, typically hierarchies. Such attributes allow knowledge discovery programs to incorporate background knowledge about...
Inductive Learning System AQ15c: The Method and User's Guide (1995)
Wnek, Janusz, Kaufman, Kenneth A., Bloedorn, Eric, Michalski, Ryszard S.
Bloedorn, Eric, Imam, Ibrahim F., Kaufman, Kenneth A., Maloof, Marcus A., Michalski, Ryszard S., Wnek, Janusz
A PartialMemory Incremental Learning Methodology and Its Application to Intrusion Detection (1995)
Marcus A. Maloof, Marcus A. Maloof, Ryszard S. Michalski, Ryszard S. Michalski
and its Application to Computer Intrusion Detection This paper discusses work in progress and introduces a partial memory incremental learning methodology. The incremental learning architecture uses...
Constructive Induction: the Key to Design Creativity (1995)
T. Arciszewski, R. S. Michalski, J. Wnek, Tomasz Arciszews Ki, Ryszard S. Michalski, Janusz Wnek
Abstract. The paper presents initial results from an emerging new direction in engineering design research, in particular, creative design. It argues that constructive induction, which was originally...
Learning as goal-driven inference (1995)
Ryszard S. Michalski, Ashwin Ram
A remarkable aspect of human learners is that they are able to apply a great variety of learning strategies in a flexible and goal-oriented manner and to dynamically accommodate the demands of...
Learning evolving concepts using a partial memory approach (1995)
Marcus A. Maloof, Ryszard S. Michalski
This paper addresses the problem of learning evolving concepts, that is, concepts whose meaning gradually evolves in time. Solving this problem is important to many applications, for example,...
Marcus A. Maloof, Ryszard S. Michalski
This paper describes a partial-memory incremental learning method based on the AQ15c inductive learning system. The method maintains a representative set of past training examples that are used...
Marcus A. Maloof, Marcus A. Maloof, Ryszard S. Michalski, Ryszard S. Michalski
This paper discusses work in progress and introduces a partial memory incremental learning methodology. The incremental learning architecture uses hypotheses induced from training examples to...
Learning Symbolic Descriptions of 2D Shapes for Object Recognition in X-Ray Images (1995)
Marcus A. Maloof, Ryszard S. Michalski
This paper describes a method for learning shape descriptions of 2D objects in x-ray images. The descriptions are induced from shape examples using the AQ15c inductive learning system. The method has...
Learning evolving concepts using a partial memory approach (1995)
Marcus A. Maloof, Ryszard S. Michalski
This paper addresses the problem of learning evolving concepts, that is, concepts whose meaning gradually evolves in time. Solving this problem is important to many applications, for example,...
Constructive Induction: the Key to Design Creativity (1995)
Tomasz Arciszewski, Ryszard S. Michalski, Janusz Wnek
Abstract. The paper presents initial results from an emerging new direction in engineering design research, in particular, creative design. It argues that constructive induction, which was originally...
Ryszard S. Michalski, Ryszard S. Michalski
This presentation consists of three interrelated parts:
Machine Vision and Learning: Research Issues and Directions (1994)
Michalski, Ryszard S., Rosenfeld, Azriel, Aloimonos, Yiannis
Machine Learning of Design Rules: Methodology and Case Study (1994)
Arciszewski, Tomasz, Bloedorn, Eric, Michalski, Ryszard S., Mustafa, Mohamad, Wnek, Janusz
Machine Learning: A Multistrategy Approach (1994)
R. S. Michalski, Ryszard S. Michalski
.... gence-C--enfx'-t,-
Hypothesis-Driven Constructive Induction in AQ17-HCI: A Method and Experiments (1994)
R. Michalski, J. Wnek, J. Wnek, R. S. Michalski, Janusz Wnek, Ryszard S. Michalski
A method for consreactive induction is described that generates new problem-relevant atu'ibutes by analyzing and abstracting iteratively created inductive concept hypotheses. The method, called...
Comparing Symbolic and Subsymbolic Learning (1994)
J. Wnek, J. Wnek, R. S. Michalski, R. S. Michalski, Janusz Wnek, Ryszard S. Michalski
This paper reports on three studies comparing symbolic and subsymbolic methods for concept learning from examples. The first study compared five learning methods, three representing symbolic learning...
Conceptual Transition from Logic to Arithmetic (1994)
Janusz Wnek, Ryszard S. Michalski
This paper presents a computational study of the change of the logic-based concepts to arithmeticbased concepts in inductive learning from examples. Specifically, we address the problem of learning...
Janusz Wnek, Ryszard S. Michalski
This paper addresses a class of learning problems that require a construction of descriptions that combine both M-of-N rules and traditional Disjunctive Normal form (DNF) rules. The presented method...
Inferential theory of learning: Developing foundations for multistrategy learning (1994)
The development of multistrategy learning systems requires a clear understanding of the roles and the applicability conditions of different learning strategies. To this end, this chapter introduces...
Conceptual Transition from Logic to Arithmetic (1994)
Janusz Wnek, Ryszard S. Michalski
This paper presents a computational study of the change of the logic-based concepts to arithmeticbased concepts in inductive learning from examples. Specifically, we address the problem of learning...
I.F.: Learning problem-oriented decision structures from decision rules: the AQDT-2 system (1994)
Ryszard S. Michalski, Ibrahim F. Imam
A decision structure is an acyclic graph that specifies an order of tests to be applied to an object (or a situation) to arrive at a decision about that object. and serves as a simple and powerful...
Inferential Theory Of Learning: Developing Foundations For Multistrategy Learning (1994)
The development of multistrategy learning systems should be based on a clear understanding of the roles and the applicability conditions of different learning strategies. To this end, this chapter...
AQ17 A Multistrategy Learning System The Method and Users Guide (1993)
Bloedorn, Eric, Wnek, Janusz, Michalski, Ryszard S., Kaufman, Kenneth A.
Machine Learning and Vision: Research Issues and Promising Directions (1993)
Michalski, Ryszard S., Pachowicz, Peter W., Rosenfeld, Azriel, Aloimonos, Yiannis
Multitype inference in multistrategy task-adaptive learning: dynamic Interlaced Hierarchies (1993)
R. S. Michalski, Michael R. Hieb, Michael R. Hieb, Ryszard S. Michalski
Research on multistrategy task-adaptive learning aims at integrating all basic inferential learning strategies--learning by deduction, induction and analogy. The implementation of such a learning...
Toward a Unified Theory of Learning: Multistrategy Task-adaptive Learning (1993)
R. S. Michalski, Ryszard S. Michalski
Any learning process can be viewed as a self-modification of the leaxnefs current knowledge tArough an. interaction with some information source. Such knowledge modification is guided by the...
Toward a Unified Theory of Learning: Multistrategy Task-adaptive Learning (1993)
Any learning process can be viewed as a self-modification of the leamer's current knowledge through an interaction with some information source. Such knowledge modification s graded by the...
Should decision trees be learned from examples or from decision rules (1993)
L F. Imam, R. Michalski, Ibrahim F. Imam, Ibrahim F. Imam, Ryszard S. Michalski, Ryszard S. Michalski
iimam @ aic.gmu.edu & michalski @ aic.gmu.edu
Kenneth Kaufman, Ryszard S. Michalski
With the rapid expansion of machine learning methods and applications, there is a strong need for computer-based interactive tools that support education in this area. The EMERALD system was...
Kenneth A. Kaufman, Ryszard S. Michalski
With the rapid expansion of machine learning methods and applications, there is a strong need for computer-based interactive tools that support education in this area. The EMERALD system was...
Constructive Induction in Engineering Design (1992)
Wnek, Janusz, Bloedorn, Eric, Arciszewski, Tomasz, Mustafa, Mohamad, Michalski, Ryszard S.
Searching for Knowledge in Large Databases (1992)
Michalski, Ryszard S., Kerschberg, Larry, Kaufman, Kenneth A., Ribeiro, James S.
A Brief Review of AQ Learning Programs and Their Application to the MONKS Problems (1992)
Bala, Jerzy W., Bloedorn, Eric, De Jong, Kenneth A., Kaufman, Kenneth A., Michalski, Ryszard S., Pachowicz, Peter W., ...
Constructive Induction in Structural Design (1992)
T. Arciszewski, T. Arciszewski, E. Bloedorn, E. Bloedorn, R. S. Michalski, R. S. Michalski, ...
by
The principal axes method for constructive induction (1992)
J. Bala, R. S. Michalski, J. Wnek, Jerzy W. Bala, Ryszard S. Michalski, Janusz Wnek
The paper describes a novel method for consreactive induction, called PRAX (Principal Axes). The madeflying idea of the method is to determine descriptions of a class of certain basic concepts, and...
The MONK's problems: A Performance Comparison of Different Learning Algorithms (1991)
Thrun, Sebastian B., Bala, Jerzy W., Bloedorn, Eric, Bratko, Ivan, Cestnik, Bojan, Cheng, John, ...
Knowledge Extraction from Databases: Design Principles of the INLEN System (1991)
Kaufman, Kenneth A., Michalski, Ryszard S., Kerschberg, Larry
Learning Two-tiered Descriptions of Flexible Concepts: The POSEIDON System (1991)
Bergadano, Francesco, Matwin, Stan, Michalski, Ryszard S., Zhang, Jianping
The MONK's problems: A Performance Comparison of Different Learning Algorithms (1991)
Thrun, Sebastian B., Bala, Jerzy W., Bloedorn, Eric, Bratko, Ivan, Cestnik, Bojan, Cheng, John, ...
Inferential learning theory as a basis for multistrategy task-adaptive learning (1991)
Ryszard S. Michalski, Ryszard S. Michalski
michalski @ aic. gmu.edu 1
Knowledge Extraction from Databases (1991)
K. Kaufman, R. S. Michalski, L. Kerschberg, Kenneth A. Kaufman, Kenneth A. Kaufman, Ryszard S. Michalski, ...
The architecture of a large-scale system, INLEN, is presented as a methodology for the discovery of knowledge from facts. INLEN combines database, knowledge base, and machine learning methods within...
Recognition of Textural Concepts Through Multilevel Symbolic Transformations (1991)
J. Bala, R. S. Michalski, Ryszard S. Michalski
This paper presents the TEXTRAL system, used for determining structural visual properties of textures through symbolic transformations. The 'method consists of two phases: one that extracts...
Eric Bloedorn and Ryszard S. Michalski (1991)
Artificial Intelligence, Eric Bloedorn, Ryszard S. Michalski
This paper presents a method for data-driven constructive induction, which generates new problemoriented attributes by combining the original attributes according to a variety of heuristic rules. The...
Searching for Knowledge in a World Flooded with Facts (1991)
The wide availability of computer technology and large electronic storage media has led to an enormous proliferation of databases in almost every area of human endeavor. This naturally creates an...
Constructive Induction from Data in AQ17-DCI: Further Experiments (1991)
Eric Bloedorn, Ryszard S. Michalski
This paper presents a method for data-driven constructive induction, which generates new problemoriented attributes by combining the original attributes according to a variety of heuristic rules. The...
Data Driven Constructive Induction in AQ17-PRE: A Method and Experiments (1991)
Eric Bloedorn, Ryszard S. Michalski
This paper presents a method for constructive induction, in which new attributes are constructed as various functions of original attributes. Such a method is called data-driven constructive...
Toward a unified theory of learning: an outline of basic ideas (1991)
Initial results toward developing a unifying conceptual framework for characterizing diverse learning strategies and paradigms are presented. We outline the inferential theory of learning that aims...
Learning Two-Tiered Descriptions of Flexible Concepts: The POSEIDON System (1990)
Bergadano, Francesco, Matwin, Stan, Michalski, Ryszard S., Zhang, Jianping
Plausible reasoning: An outline of theory and experiments to validate its structural aspects (1990)
Michalski, Ryszard S., Dontas, Kejitan, Boehm-Davis, Deborah
R. S. Michalski, Y. Kodratoff, Ryszard S. Michalski, Yves Kodratoff
The last few years have witnessed a remarkable expansion of research in machine learning. The field has gained an unprecedented popularity, several new areas have developed, and some previously...
J. Wnek, J. Sarma, A. Wahab, R. S. Michalski, Study Single, Concept Learning, ...
Four different learning methods are experimentally compared by applying them to a series of simple, single concept learning problems. The methods compared include a rule-learning program, AQI$, a...
A Methodological Framework for Multistrategy Task-adaptive Learning (1990)
Ryszard S. Michalski, R. S. Michalski
by
J. Wnek, J. Sarma, A. Wahab, R. S. Michalski, Janusz Wnck, Ashraf A. Wahab, ...
Four different learning methods are experimentally compared by applying them to a series of simple, single concept learning problems. The methods compared include a rule-learning program, AQ15, a...
Learning Flexible Concepts Through a Search for Simpler but Still Accurate Descriptions (1989)
Bergadano, Francesco, Matwin, Stan, Zhang, Jianping, Michalski, Ryszard S.
Plausible Reasoning: An Outline of Theory and Experiments (1989)
Dontas, Kejitan, Boehm-Davis, Deborah, Michalski, Ryszard S.
Mining for Knowledge in Databases: Goals and General Description of the INLEN System (1989)
Kaufman, Kenneth A., Michalski, Ryszard S., Kerschberg, Larry A.
The INLEN System for Extracting Knowledge from Databases: Goals and General Description (1989)
Kaufman, Kenneth A., Michalski, Ryszard S., Zytkow, Jan M., Kerschberg, Larry
R. S. Michalski, Ryszard S. Michalski
Existing approaches to the recognition problem assume that to recognize a concept one needs to match the observed data with a stored description of the concept. When the properties of the observed...
Rule Optimization via SG-TRUNC method (1989)
J. Zhang, R. S. Michalski, Jianping Zhang, Ryszard S. Michalski
Most inductive learning systems generate complete and consistent descriptions. In order to achieve completeness and consistency in the presence of noise or iraprecision, one may generate overly...
A General Criterion for Measuring Quality of Concept Descriptions (1988)
Bergadano, Francesco, Matwin, Stan, Michalski, Ryszard S., Zhang, Jianping
Measuring Quality of Concept Descriptions (1988)
Bergadano, Francesco, Matwin, Stan, Zhang, Jianping, Michalski, Ryszard S.
Machine Learning in a Dynamic World: Panel Discussion (1988)
Antsaklis, Panos J., DeJong, Kenneth A., Meyrowitz, Alan L., Meystel, Alexander M., Michalski, Ryszard S., Sutton, Richard S.
AgAssistant: An Experimental Expert System Builder for Agricultural Applications (1987)
Katz, Bruce, Fermanian, Thomas W., Michalski, Ryszard S.
This research was supported in part by the International Intelligent Systems, Inc.; the University of Illinois Research Board; Project No. ILLU-65-0357, of the Agricultural Experiment Station,...
The ADVISE.1 Meta-Expert System: The General Design and a Technical Description (1987)
Michalski, Ryszard S., Baskin, Arthur B., Uhrik, Carl T., Channic, Tom D.
Douglas L. Medin, William D. Wattenmaker, Ryszard S. Michalski
The paper examines constraints ond preferences employed by people in learning decision rules from preclossified examples. Results from four experiments with human subiects were onolyzed ond compared...
The AQ15 Inductive Learning System: An Overview and Experiments (1986)
Michalski, Ryszard S., Mozetic, Igor, Hong, Jiarong, Lavrac, Nada
This research was supported in part by the National Science Foundation under Grant No. DCR 84-06801, the Office of Naval Research under Grant No. N00014-82-K-0186, the Defense Advanced Research...
Machine Learning: Challenges of the Eighties (1986)
Michalski, Ryszard S., Amarel, Saul, Lenat, Douglas B., Michie, Donald, Winston, Patrick H.
Integrating Quantitative and Qualitative Discovery: The ABACUS System (1986)
Falkenhainer, Brian C., Michalski, Ryszard S.
This research was supported in part by the National Science Foundation under Grant No. DCR 84-06801, Office of Naval Research under Grant No. N00014-82-K-0186, and Defense Advanced Research Project...
Machine Learning: An Artificial Intelligence Approach Vol. II (1986)
Michalski, Ryszard S. (comp.), Carbonell, Jaime G. (comp.), Mitchell, Tom M. (comp.)
Michalski, Ryszard S., Ko, Heedong, Chen, Kaihu
This work was supported in part by the National Science Foundation under grant DCR 84-06801, the Office of Naval Research under grant N00014-82-K-0186, and the Defense Advanced Research Projects...
Dynamic Recognition: An Outline of Theory of How to Recognize Concepts without Matching Rules (1986)
This research was supported in part by the National Science Foundation under grant No. DCR-8406801, by the Defense Advanced Research Project Agency under Grant No. N00014-K-85-0878, and by the Office...
Michalski, Ryszard S., Ko, Heedong, Chen, Kaihu
This research was supported in part by the National Science Foundation under grant DCR 84-06801, the Office of Naval Research under grant N00014-82-K-0186, and the Defense Advanced Research Projects...
Understanding the nature of learning: Issues and research directions (1986)
Ryszard S. Michalski, Ryszard S. Michalski
This chapter presents an overview of goals and directions in machine learning research, and is intended to serve as a conceptual road map to other chapters. It investigates intrinsic aspects of the...
Conceptual Clustering: Inventing Goal-Oriented Classifications of Structured Objects (1986)
R. S. Michalski, T. Mitchell, J. Carbonell (eds, Ryszard S. Michalski, Ryszard S. Michalski, Of Technologv I
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Variable Precision Logic (1985)
Michalski, Ryszard S., Winston, Patrick H.
Variable precision logic is concerned with problems of reasoning with incomplete information and under time constraints. It offers mechanisms for handling trade-offs between the precision of...
Variable Precision Logic (1985)
Michalski, Ryszard S., Winston, Patrick H.
Variable precision logic is concerned with problems of reasoning with incomplete information and under time constraints. It offers mechanisms for handling trade-offs between the precision of...
Knowledge Repair Mechanisms: Evolution vs Revolution (1985)
This research was supported in part by the National Science Foundation under grant DCR 84-06801, and in part by the Office of Naval Research under grant N00014-82-K-0186.
An Expert System to Assist Turfgrass Managers in Weed Identification (1985)
Fermanian, Thomas W., Michalski, Ryszard S., Katz, Bruce
This work is supported in part by grants from ONR no. N00014-82-K-0186, National Science Foundation no. NSF DCR 84-06801, project No. 65-0267 of the Agric. Exp. Stn., College of Agric., Univ. of...
Knowledge Repair Mechanisms: Evolution vs Revolution (1985)
This research was supported in part by the National Science Foundation under grant DCR 84-06801, and in part by the Office of Naval Research under grant N00014-82-K-0186.
SPARC/E(V.2), An Eleusis Rule Generator and Game Player (1985)
Michalski, Ryszard S., Ko, Heedong, Chen, Kaihu
This research was supported in part by the National Science Foundation under grant NSF DCR 84-06801.
Discovering patterns in sequences of events (1985)
Tom Dietterich, Thomas G. Dietterich, Ryszard S. Michalski, Ryszard S. Michalski
Given a sequence of events (or ob]ects), each 'characterized by a set of attributes, the problem considered is to discover a rule characterizing the sequence and able to predict a plausible...
Automated Construction of Classifications: Conceptual Clustering versus Numerical Taxonomy (1983)
Michalski, Ryszard S., Stepp, Robert E.
This research was supported in part by the National Science Foundation under grant MCS-82-05166 and in part by the Office of Naval Research under Grant N00014-82-K-0186.
Inductive Learning: A Review of Some Recent Work (1983)
This research was supported in part by the National Science Foundation under grant No. MCS 82-05166.
Discovering Patterns in Sequences of Events (1983)
Dietterich, Thomas G., Michalski, Ryszard S.
The authors gratefully acknowledge the partial support of the NSF under grant MCS-82-05166 and of the Office of Naval Research under grant No. N00014-82-K-0186.
Conjunctive Conceptual Clustering: Classification Using Background Knowledge (1983)
Stepp, Robert E., Michalski, Ryszard S.
Abstract only.
INDUCE 2: A Program for Learning Structural Descriptions from Examples (1983)
Hoff, William A., Michalski, Ryszard S., Stepp, Robert E.
This work was supported in part by the National Science Foundation, Grant No. NSF MCS 82-05166.
This work was supported in part by the National Science Foundation under Grants MCS 76-22940, MCS 82-05166, and MCS 82-05896, and the Office of Naval Research under Grant N00014-82-K-0186.
Stauffer, M. D., Michalski, Ryszard S.
This work was supported in part by the National Science Foundation Grants No. MCS 82-05896 and MCS 02-05166.
PLANT/DS: An Expert Consulting System for the Diagnosis of Soybean Diseases (1982)
Michalski, Ryszard S., Davis, J. H., Bisht, V. S., Sinclair, J. B.
Knowledge based programming assistant, KBPA-1 (1982)
Badger, D. G., Campbell, R., Dershowitz, N., Harandi, M. T., Laursen, A., Michalski, Ryszard S., ...
A Method of Organizing Data into Conceptual Hierarchies (1981)
Stepp, Robert E., Michalski, Ryszard S.
Abstract only.
Michalski, Ryszard S., Larson, James B.
This work was supported in part by a National Science Foundation Grant NSF MCS 76-22940 and in part by a Senior Visiting Fellowship from British Science Research Council.
Designing Extended Entry Decision Tables and Optimal Decision Trees Using Decision Diagrams (1978)
This work was supported in part by the National Science Foundation under grant NSF MCS 76-22940.
This work was supported in part by the National Science Foundation under grant NSF MCS 76-22940.
A System of Programs for Computer-Aided Induction: A Summary (1977)
The research described in this paper was supported in part by the National Science Foundation, Grant NSF MCS 74-03514.
Toward Computer-Aided Induction: A Brief Review of Currently Implemented AQVAL Programs (1977)
This work was supported in part by the National Science Foundation, Washington, DC, under grant no. NSF MCS 74-03514.
This work was supported by US PHS MB 00114 from Medical Information Systems Laboratory.
AQVAL/1 (AQ7) User's Guide and Program Description (1975)
Michalski, Ryszard S., Larson, James
This work was supported in part by the National Science Foundation under Grant No. DCR 74-03514.
Variable-Valued Logic: System VL1 (1974)
The paper defines the concept of a variable-valued logic (VL) system and discusses in detail one specific VL system called VL1. The main motivation for the development of the VL system concept is to...