Advances in Machine Learning I

1. Front Matter

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2. Introductory Chapters

   1. Front Matter

      Pages 1-1

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   2. Ryszard S. Michalski: The Vision and Evolution of Machine Learning

      • Janusz Wojtusiak, Kenneth A. Kaufman

      Pages 3-22

   3. The AQ Methods for Concept Drift

      • Marcus A. Maloof

      Pages 23-47

   4. Machine Learning Algorithms Inspired by the Work of Ryszard Spencer Michalski

      • Krzysztof J. Cios, Łukasz A. Kurgan

      Pages 49-74

   5. Inductive Learning: A Combinatorial Optimization Approach

      • Janusz Kacprzyk, Grażyna Szkatuła

      Pages 75-93

3. General Issues

   1. Front Matter

      Pages 95-95

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   2. From Active to Proactive Learning Methods

      • Pinar Donmez, Jaime G. Carbonell

      Pages 97-120

   3. Explicit Feature Construction and Manipulation for Covering Rule Learning Algorithms

      • Nada Lavrač, Johannes Fürnkranz, Dragan Gamberger

      Pages 121-146

   4. Transfer Learning via Advice Taking

      • Lisa Torrey, Jude Shavlik, Trevor Walker, Richard Maclin

      Pages 147-170

4. Classification and Beyond

   1. Front Matter

      Pages 171-171

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   2. Determining the Best Classification Algorithm with Recourse to Sampling and Metalearning

      • Pavel Brazdil, Rui Leite

      Pages 173-188

   3. Transductive Learning for Spatial Data Classification

      • Michelangelo Ceci, Annalisa Appice, Donato Malerba

      Pages 189-207

   4. Beyond Sequential Covering – Boosted Decision Rules

      • Krzysztof Dembczyński, Wojciech Kotłowski, Roman Słowiński

      Pages 209-225

   5. An Analysis of Relevance Vector Machine Regression

      • Matti Saarela, Tapio Elomaa, Keijo Ruohonen

      Pages 227-246

   6. Cascade Classifiers for Hierarchical Decision Systems

      • Zbigniew W. Raś, Agnieszka Dardzińska, Wenxin Jiang

      Pages 247-256

   7. Creating Rule Ensembles from Automatically-Evolved Rule Induction Algorithms

      • Gisele L. Pappa, Alex A. Freitas

      Pages 257-273

   8. Structured Hidden Markov Model versus String Kernel Machines for Symbolic Sequence Classification

      • Ugo Galassi, Marco Botta, Lorenza Saitta

      Pages 275-295

5. Soft Computing

   1. Front Matter

      Pages 297-297

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   2. Partition Measures for Data Mining

      • Ronald R. Yager

      Pages 299-319

Professor Richard S. Michalski passed away on September 20, 2007. Once we learned about his untimely death we immediately realized that we would no longer have with us a truly exceptional scholar and researcher who for several decades had been inf- encing the work of numerous scientists all over the world - not only in his area of expertise, notably machine learning, but also in the broadly understood areas of data analysis, data mining, knowledge discovery and many others. In fact, his influence was even much broader due to his creative vision, integrity, scientific excellence and exceptionally wide intellectual horizons which extended to history, political science and arts. Professor Michalski’s death was a particularly deep loss to the whole Polish sci- tific community and the Polish Academy of Sciences in particular. After graduation, he began his research career at the Institute of Automatic Control, Polish Academy of Science in Warsaw. In 1970 he left his native country and hold various prestigious positions at top US universities. His research gained impetus and he soon established himself as a world authority in his areas of interest – notably, he was widely cons- ered a father of machine learning.

• autonomous robot
• classification
• data analysis
• data mining
• fuzzy
• hidden markov model
• knowledge discovery
• learning
• machine learning
• optimization
• reinforcement learning
• robot
• robotics

• Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland

  Jacek Koronacki, Sławomir T. Wierzchoń

• Woodward Hall 430C University of North Carolina, Charlotte, USA

  Zbigniew W. Raś

• Systems Research Institute, Polish Academy of Sciences, Warsaw, 01-447

  Janusz Kacprzyk

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