Eixtei Bruce, G. Buchanan, Dad C. Wilkim, Morgan Kaufmann Publishers, Jc Graphics, ...
acquisition da and learning
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...
E. Bloedorn, I. Imam, K. Kaufman, M. Maloof, R. S. Michalski, J. Wnek, ...
The "East-West Challenge" is the title of the second international competition of machine learning programs, organized in the Fall 1994 by Donald Michie, Stephen Muggleton, David Page and...
Integrating MuP. ipl Knowledge Representations and Learning (2007)
Ryszard Michalski, Arthur B. Buskin, R. S. Michalski, A. B. Baskin
The ADVISE system is an integrated set of tools for the development of and experimentation with expert sys-tems iu various specific application domains. It functions as a multi-purpose inference...
R. R. Sokal, Numerical Taxonomy, The Principles, Practice Of, ...
Figure 6: An MTAH for 11 lung tumor contours generated by M-DISC based on area, circularity, and extrusiveness. Figure 7: The CT scanned lung image for image 6 (in Figure 3) with the lung tumor...
describing and predicting discrete processes (2007)
R. S. Michalski, H. Ko, K. Chen, Ryszard Michalski, Heedong Ko, Kaihu Chen
Abstract. Qualitative prediction is concerned with problems of building symbolic descriptions of processes, and using these descriptions for predicting a plausible continuation of these processes. It...
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
An Extension Matrix Approach to;he General Covering Problem* (2007)
J. Hong, R. S. Michalski, C. Uhrik, Jiarong Hong, Ryszard Michelski, Carl Uhrik, ...
new approach, called the czech. sion matrix (EM) approach, for describing and solving the general cot'erin9 problem is proposed. The paper emphasizes that the GCP is Nrp-hard and describes an...
SYMBOLIC COMPUTATION- Artificial Intelligence (2007)
Ryszard S. Michalski, Leonard Bolc (ed, G. L. Bradshaw, P. Langley, R. S. Michalski, S. Ohlsson, ...
139 figs., 1982
An Eleusis, Rule Generator, R. S. Ko, R. S. Michalski, R. S. Michalski, ...
publication in "Cognitive Science."
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
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....
F. Bergadano, F. Bergadano, S. Matwin, S. Matwin, R. S. Michalski, R. S. Michalski, ...
A method for learning flexible concepts is described, that is concepts that are imprecise and context dependent. The method is based on a two-tiered concept representation. In such a representation...
H. Ko, H. Ko, R. S. Michalski, R. S. Michalski
The paper discusses a distinction between the knowledge used by an' explanation process (explanation knowledge) and the structure built as a result of this process (explanation structure). An...
R. S. Michalski, R. S. Michalski, J. Wnek, J. Wnek, J. Wnek, R. S. Michaiski
Submitted to the IJCAJ-91 Workahop on
Architecture, Initial Implementation and First Results (2007)
J. S. Ribeiro, R. S. Michalski, R. S. Michalski, L. Kerschberg, L. Kerschberg, K. A. Kaufman, ...
Abstract. The architecture of an intelligent multistrategy assistant for knowledge discovery from facts, INLEN, is described and illustrated by an exploratory application. INLEN integrates a...
E. Bloedorn, I. Imam, K. Kaufman, M. Maloof, R. S. Michalski, J. Wnek, ...
The “East-West Challenge ” is the title of the second international competition of machine learning programs, organized in the Fall 1994 by Donald Michie, Stephen Muggleton, David Page and Ashwin...
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),...
Plausible Reasoning: An Outline of Theory and Experiments (2007)
R. S. Michalski, R. S. Michalski, K. Dontas, K. Dontas, D. Boehm-davis, D. Boehm-davis
This chapter presents a brief review of a computational theory of human plausible reasoning developed by Collins and Michalski, and discusses experiments conducted toward its validation. This is a...
Submitted for publication in Machine Learning Journal, 1991 LEARNING TWO-TIERED DESCRIPTIONS (2007)
R. Michalski, F. Bergadano, F. Bergadano, F. Bergadano, S. Matwin, S. Matwin, ...
email: jzhang @ gmuvax2.gmu.edu Machine learning research has so far been primarily concerned with learning crisp concepts, that is concepts that are well-defined and context-independent. Most...
AN APPLICATION OF CONSTRUCTIVE INDUCTION TO ENGINEERING DESIGN (2007)
J Wnek, J. Wnek, J. Wnek, R. S. Michalski, R. S. Michalski, R. S. Michalski, ...
by
Concepts as Flexible and Context-dependent Sets: The Two-tiered View* (2007)
The paper proposes to view concepts as sets with flexible and context-dependent boundaries. An efficient representation of such concepts consists of two components (tiers), an explicit one, called...
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...
J. Wnek, J. Wnek, J. Wnek, R. S. Michalski, R. S. Michalski, R. S. Michalski
This paper presents a method for constructive induction in which new problem-relevant attributes are generated by analyzing iteratively created inductive hypotheses. The method starts by creating a...
LEARNING FLEXIBLE CONCEPTS USING A TWO-TIERED REPRESENTATION (2007)
R. S. Michalski, R. S. Michalski, F. Bergadano, F. Bergadano, S. Matwin, S. Matwin, ...
'Most human concepts are flexible in the sense that they inherently lack: precise boundaries, and these boundaries are often contextdependent. This chapter describes 'a method for...
KNOWLEDGE IN LARGE DATA BASES (2007)
R. S. Michalski, L Kerschberg, K. Kaufman, J. Ribeiro, J. S. Ribeiro
Abstract: Among the central tasks in the development of expert systems is the formulation, debugging and implementation of a knowledge base. The knowledge encoded in the knowledge base is usually...
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...
Running head: Plausible Reasoning Plausible Reasoning (2007)
D. Boehm-davis, K. Dontas, R. S. Michalski
Collins & Michalski (1989) developed a descriptive theory pof plausible reasoning that provides a formal framework, a language, and a computational model for describing human plausible reasoning...
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,...
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
R. Michalski, F. Bergadano, F. Bergadano, S. Matwin, R. S. Michalski, J. Zhang, ...
This paper describes a method for learning flexible concepts, by which are meant concepts that lack precise definition and are context-dependent. To describe such concepts, the method employs a...
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...
IDENTIFICATION OF GRASSES IN TURF (2007)
T. W. Fermanian, R. Michalski, W. W. Fermanian, R. S. Michalski
To effectively control weeds found in a turf it is first necessary to correctly identify them. A computer program, WEEDER, was built uslng the artificial intelligence system AGASSISTANT to provide a...
J. Wojtusiak, R. S. Michalski, K. A. Kaufman, J. Pietrzykowski
The AQ21 program aims to perform natural induction, a process of generating inductive hypotheses in humanoriented forms that are easy to interpret and understand. This is achieved by employing a...
Optimizing complex systems by intelligent evolution: (2006)
the LEMd method and case study
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...
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...
Computer Vision through Learning (1998)
Michalski, R. S., Rosenfeld, A., Aloimonos, Y., Duric, Z., Maloof, M.
The underlying motivation for this research is that vision systems need learning capabilities for handling problems for which algorithmic solutions are unknown or difficult to obtain. In this...
Data-driven constructive induction (1998)
E. Bloedorn, E. Bioedorn, J. Wnek, J. Wnek, R. S. Michalski, R. S. Michalski
This paper presents a method for multistrategy constructive induction that integrates two inferential learning strategies---empirical induction and deduction, and two computational methodsdatadriven...
A Collaborative, Effort George, R. S. Michalski, A. Rosenfeld, Y. Aloimonos, Z. Duric, ...
This report briefly reviews research progress on vision through learning conducted as a collaborative effort of the GMU Machine Learning and Inference Laboratory and the UMD Computer Vision...
Recognizing Blasting Caps in X-Ray Images (1996)
M. A. Maloof, Z. Duric, R. S. Michalski, A. Rosenfeld
This paper presents work in progress on an approach to the problem of recognizing blasting caps in x-ray images. An analysis of functional properties of blasting caps was used to design the...
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...
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...
M. A. Maloof, R. S. Michalski, An Initial Study
This paper describes a method for applying AQ15c to learning shape descriptions of 2D bloblike objects in x-ray images. The methodology and initial experimental results are discussed, along with...
M. A. Maloof, R. S. Michalski, An Initial Study
This paper describes a method for applying AQ15c to learning shape descriptions of 2D bloblike objects in x-ray images. The methodology and initial experimental results are discussed, along with...
I. F. Imam, I. F. Imam, R. S. Michalski, R. S. Michalski, L. Kerschberg, L. Kerschberg
The paper presents a method for integrating two different data analysis techniques: symbolic learning and statistical. The method concerns the problem of discovering rules characterizing the...
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...
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...
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...
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 endearour. This naturally creates an...
A Method for Multistrategy Task-adaptive Learning Based on Plausible Justifications (1991)
R. S. Michalski, G. Tecuci, Gheorghe D. Tecucl
Multistrategy task-adaptive learning (MTL) comprises a class of methods in which the learner determines by itself which strategy or combination of strategies is most appropriate for a given learning...
The MONK's Problems A Performance Comparison of Different Learning Algorithms (1991)
S. B. Thrun, J. Bala, E. Bloedorn, I. Bratko, B. Cestnik, J. Cheng, ...
This report summarizes a comparison of different learning techniques which was performed at the 2nd European Summer School on Machine Learning, held in Belgium during summer 1991. A variety of...
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...
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...
The logic of plausible reasoning (1989)
A. Collins, R. S. Michalski, A Core Theory, A Core Theory, Allan Collins, Bolt Beranek, ...
The paper presents a core theory of human plausible reasoning based on analysis of people's answers ta everyday questions about the world. The theory cansists of three parts: 1. a farmal...
A. B. Baskin, R. S. Michalski, G. Guida, C. Tasso (editors, Ryszarcl S. Michaiki
Over the last few years, knowledge-based systems have clearly demonstrated the potential for substantial impact in a number of diverse areas. The successful construction of such knowledge-based...
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
Lq'vrity lllins
A Logic-based Approach to Conceptual Database Analysis (1982)
R. S. Michalski, R. S. Michalski, A. B. Baskin, A. B. Baskin, K. A. Spackman
by