English
资源说明：This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of “boosting,” how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms.
Table of Contents
Chapter 1 A Simple Machine-Learning Task
Chapter 2 Probabilities: Bayesian Classifiers
Chapter 3 Similarities: Nearest-Neighbor Classifiers
Chapter 4 Inter-Class Boundaries: Linear and Polynomial Classifiers
Chapter 5 Artificial Neural Networks
Chapter 6 Decision Trees
Chapter 7 Computational Learning Theory
Chapter 8 A Few Instructive Applications
Chapter 9 Induction of Voting Assemblies
Chapter 10 Some Practical Aspects to Know About
Chapter 11 Performance Evaluation
Chapter 12 Statistical Significance
Chapter 13 The Genetic Algorithm
Chapter 14 Reinforcement Learning
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