Advances in Machine Learning I: Dedicated to the Memory of Professor Ryszard S. Michalski

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Jacek Koronacki, Zbigniew W Ras, Slawomir T. Wierzchon
Springer Science & Business Media, Feb 4, 2010 - Computers - 521 pages
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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.
 

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Contents

The Vision and Evolution of Machine Learning
3
The AQ Methods for Concept Drift
23
Machine Learning Algorithms Inspired by the Work of Ryszard Spencer Michalski
49
A Combinatorial Optimization Approach
75
General Issues
94
From Active to Proactive Learning Methods
97
Explicit Feature Construction and Manipulation for Covering Rule Learning Algorithms
121
Transfer Learning via Advice Taking
147
Partition Measures for Data Mining
299
An Analysis of the FURIA Algorithm for Fuzzy Rule Induction
320
Increasing Incompleteness of Data Sets A Strategy for Inducing Better Rule Sets
345
Knowledge Discovery Using Rough Set Theory
367
Image Diagnosis
384
Segmentation of Breast Cancer Fine Needle Biopsy Cytological Images Using Fuzzy Clustering
405
Machine Learning for Robotics
418
Automatic Selection of Object Recognition Methods Using Reinforcement Learning
421

Classification and Beyond
171
Determining the Best Classification Algorithm with Recourse to Sampling and Metalearning
172
Transductive Learning for Spatial Data Classification
189
Beyond Sequential Covering Boosted Decision Rules
209
An Analysis of Relevance Vector Machine Regression
226
Cascade Classifiers for Hierarchical Decision Systems
247
Creating Rule Ensembles from AutomaticallyEvolved Rule Induction Algorithms
257
Structured Hidden Markov Model versus String Kernel Machines for Symbolic Sequence Classification
275
Soft Computing
296
Comparison of Machine Learning for Autonomous Robot Discovery
440
Multistrategy Learning for Robot Behaviours
457
Neural Networks and Other Nature Inspired Approaches
477
Quo Vadis? Reliable and Practical Rule Extraction from Neural Networks
479
Learning and Evolution of Autonomous Adaptive Agents
491
Learning and Unlearning in HopfieldLike Neural Network Performing Boolean Factor Analysis
501
Author Index
519
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