J. Wnek

Publication List Details

Period

1990 - 2009

Number

19

Co-Authors

REFERENCES (2009)

D. Ackley, G. Hinton, T. Sejnowski, E. Farguell, F. Mazzanti, E. Gómez-ramírez, ...

HOD process. Therefore, there is a tradeoff between memory and computing time. HOD provides a direct solution for the learning algorithm. In comparison, tuning the MC algorithm to provide lower error...

The MONK's Problems APerformance Comparison of Di erent Learning Algorithms (2008)

S. B. Thrun, J. Bala, E. Bloedorn, I. Bratko, B. Cestnik, J. Cheng, ...

Once upon a time, in July 1991, the monks of Corsendonk Priory were faced with a school held in their priory, namely the 2 nd European Summer School on Machine Learning. After listening more than one...

An Automated Conversion of Documents Containing Math into SGML (Extended Abstract) (2007)

J. Wnek, Robert Price

Janusz Wnek and Robert Price Science Applications International Corporation 1953 Gallows Road Vienna, VA 22182 Abstract Intelligent document understanding (IDU) systems convert scanned document pages...

How Did Aq Face The East-West Challenge? An Analysis of the AQ Family's Performance in the 2nd International Competition of Machine Learning Programs (2007)

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...

Acknowledgment (2007)

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...

R.S.,"An Experimental Comparison of Symbolic and Subsymbolic Learning Paradigms: Phase I - Learning Logic-style Concepts (2007)

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),...

Evaluating and Changing Representation in Machine Learning HYPOTHESIS-DRIVEN CONSTRUCTIVE INDUCTION IN AQ17: A Method and Experiments (2007)

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...

R.S.,"An Experimental Comparison of Symbolic and Subsymbolic Learning Paradigms: Phase I - Learning Logic-style Concepts (2007)

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...

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...

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...

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...

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)

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...

Comparing Learning Paradigms via Diagrammatic Visualization: A Case Study in Single Concept Learning using Symbolic, Neural Net and Genetic Algorithm Methods (1990)

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...

Comparing Learning Paradigms via Diagrammatic Visualization: A Case Study in Single Concept Learning using Symbolic, Neural Net and Genetic Algorithm Methods (1990)

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...