Advances in Machine Learning I

Introductory Chapters.- Ryszard S. Michalski: The Vision and Evolution of Machine Learning.- The AQ Methods for Concept Drift.- Machine Learning Algorithms Inspired by the Work of Ryszard Spencer Michalski.- Inductive Learning: A Combinatorial Optimization Approach.- General Issues.- From Active to Proactive Learning Methods.- Explicit Feature Construction and Manipulation for Covering Rule Learning Algorithms.- Transfer Learning via Advice Taking.- Classification and Beyond.- Determining the Best Classification Algorithm with Recourse to Sampling and Metalearning.- Transductive Learning for Spatial Data Classification.- Beyond Sequential Covering - Boosted Decision Rules.- An Analysis of Relevance Vector Machine Regression.- Cascade Classifiers for Hierarchical Decision Systems.- Creating Rule Ensembles from Automatically-Evolved Rule Induction Algorithms.- Structured Hidden Markov Model versus String Kernel Machines for Symbolic Sequence Classification.- Soft Computing.- Partition Measures for Data Mining.- An Analysis of the FURIA Algorithm for Fuzzy Rule Induction.- Increasing Incompleteness of Data Sets-A Strategy for Inducing Better Rule Sets.- Knowledge Discovery Using Rough Set Theory.- Machine Learning Techniques for Prostate Ultrasound Image Diagnosis.- Segmentation of Breast Cancer Fine Needle Biopsy Cytological Images Using Fuzzy Clustering.- Machine Learning for Robotics.- Automatic Selection of Object Recognition Methods Using Reinforcement Learning.- Comparison of Machine Learning for Autonomous Robot Discovery.- Multistrategy Learning for Robot Behaviours.- Neural Networks and Other Nature Inspired Approaches.- Quo Vadis? Reliable and Practical Rule Extraction from Neural Networks.- Learning and Evolution of Autonomous Adaptive Agents.- Learning andUnlearning in Hopfield-Like Neural Network Performing Boolean Factor Analysis.