Advanced Lectures on Machine Learning

by Mendelson, S.; Smola, Alexander J.

• ISBN13: 9783540005292
• ISBN10: 3540005293
• Format: Paperback
• Copyright: 2003-04-01
• Publisher: Springer-Verlag New York Inc

Summary

This book presents revised reviewed versions of lectures given during the Machine Learning Summer School held in Canberra, Australia, in February 2002. The lectures address the following key topics in algorithmic learning: statistical learning theory, kernel methods, boosting, reinforcement learning, theory learning, association rule learning, and learning linear classifier systems. Thus, the book is well balanced between classical topics and new approaches in machine learning. Advanced students and lecturers will find this book a coherent in-depth overview of this exciting area, while researchers will use this book as a valuable source of reference.

Table of Contents

A Few Notes on Statistical Learning Theory	p. 1
A Short Introduction to Learning with Kernels	p. 41
Bayesian Kernel Methods	p. 65
An Introduction to Boosting and Leveraging	p. 118
An Introduction to Reinforcement Learning Theory: Value Function Methods	p. 184
Learning Comprehensible Theories from Structured Data	p. 203
Algorithms for Association Rules	p. 226
Online Learning of Linear Classifiers	p. 235
Author Index	p. 259
Table of Contents provided by Blackwell. All Rights Reserved.