文件名称:
An.Introduction.to.Machine.Learning
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文件大小: 2mb
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上传时间: 2016-12-07
详细说明： 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 classifi ers, 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 ...展开收缩
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