Introductory Chapters

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  Janusz Wojtusiak, Kenneth A. Kaufman: Ryszard S. Michalski: The Vision and Evolution of Machine Learning. 3-22
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  Marcus A. Maloof: The AQ Methods for Concept Drift. 23-47
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  Krzysztof J. Cios, Lukasz A. Kurgan: Machine Learning Algorithms Inspired by the Work of Ryszard Spencer Michalski. 49-74
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  Janusz Kacprzyk, Grazyna Szkatula: Inductive Learning: A Combinatorial Optimization Approach. 75-93

General Issues

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  Pinar Donmez, Jaime G. Carbonell: From Active to Proactive Learning Methods. 97-120
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  Nada Lavrac, Johannes Fürnkranz, Dragan Gamberger: Explicit Feature Construction and Manipulation for Covering Rule Learning Algorithms. 121-146
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  Lisa Torrey, Jude W. Shavlik, Trevor Walker, Richard Maclin: Transfer Learning via Advice Taking. 147-170

Classification and Beyond

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  Pavel Brazdil, Rui Leite: Determining the Best Classification Algorithm with Recourse to Sampling and Metalearning. 173-188
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  Michelangelo Ceci, Annalisa Appice, Donato Malerba: Transductive Learning for Spatial Data Classification. 189-207
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  Krzysztof Dembczynski, Wojciech Kotlowski, Roman Slowinski: Beyond Sequential Covering - Boosted Decision Rules. 209-225
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  Matti Saarela, Tapio Elomaa, Keijo Ruohonen: An Analysis of Relevance Vector Machine Regression. 227-246
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  Zbigniew W. Ras, Agnieszka Dardzinska, Wenxin Jiang: Cascade Classifiers for Hierarchical Decision Systems. 247-256
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  Gisele L. Pappa, Alex Alves Freitas: Creating Rule Ensembles from Automatically-Evolved Rule Induction Algorithms. 257-273
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  Ugo Galassi, Marco Botta, Lorenza Saitta: Structured Hidden Markov Model versus String Kernel Machines for Symbolic Sequence Classification. 275-295

Soft Computing

• 
  Ronald R. Yager: Partition Measures for Data Mining. 299-319
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  Jens Christian Hühn, Eyke Hüllermeier: An Analysis of the FURIA Algorithm for Fuzzy Rule Induction. 321-344
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  Jerzy W. Grzymala-Busse, Witold J. Grzymala-Busse: Increasing Incompleteness of Data Sets - A Strategy for Inducing Better Rule Sets. 345-365
• 
  Yaile Caballero, Rafael Bello, Leticia Arco, María M. García, Enislay Ramentol: Knowledge Discovery Using Rough Set Theory. 367-383
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  Aboul Ella Hassanien, Hameed Al-Qaheri, Václav Snásel, James F. Peters: Machine Learning Techniques for Prostate Ultrasound Image Diagnosis. 385-403
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  Marek Kowal, Józef Korbicz: Segmentation of Breast Cancer Fine Needle Biopsy Cytological Images Using Fuzzy Clustering. 405-417

Machine Learning for Robotics

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  Reinaldo A. C. Bianchi, Arnau Ramisa, Ramon López de Mántaras: Automatic Selection of Object Recognition Methods Using Reinforcement Learning. 421-439
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  Ivan Bratko: Comparison of Machine Learning for Autonomous Robot Discovery. 441-456
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  Claude Sammut, Tak Fai Yik: Multistrategy Learning for Robot Behaviours. 457-476

Neural Networks and Other Nature Inspired Approaches

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  Joachim Diederich, Alan B. Tickle, Shlomo Geva: Quo Vadis? Reliable and Practical Rule Extraction from Neural Networks. 479-490
• 
  Vladimir G. Red'ko, Danil V. Prokhorov: Learning and Evolution of Autonomous Adaptive Agents. 491-500
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  Alexander A. Frolov, Dusan Húsek, Igor P. Muraviev, Pavel Yu. Polyakov: Learning and Unlearning in Hopfield-Like Neural Network Performing Boolean Factor Analysis. 501-518

last updated on Sat May 25 04:43:40 2013 by the dblp team
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