Advances in Machine Learning I

Dedicated to the Memory of Professor Ryszard S. Michalski
1. Auflage. 2010
ISBN: 978-3-642-05176-0
Verlag: Springer

Buch, Englisch, Band 262, 521 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1074 g

Reihe: Studies in Computational Intelligence

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.

Zielgruppe

Research

Autoren/Hrsg.

Koronacki, Jacek

Wierzchon, Slawomir T.

Ras, Zbigniew W.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining

Inhaltsverzeichnis

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.