An introduction to machine learning
著者
書誌事項
An introduction to machine learning
Springer, c2015
大学図書館所蔵 件 / 全8件
-
該当する所蔵館はありません
- すべての絞り込み条件を解除する
注記
Bibliography: p. 287-290
Includes index
内容説明・目次
内容説明
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.
目次
A Simple Machine-Learning Task.- Probabilities: Bayesian Classifiers.- Similarities: Nearest-Neighbor Classifiers.- Inter-Class Boundaries: Linear and Polynomial Classifiers.- Artificial Neural Networks.- Decision Trees.- Computational Learning Theory.- A Few Instructive Applications.- Induction of Voting Assemblies.- Some Practical Aspects to Know About.- Performance Evaluation.-Statistical Significance.- The Genetic Algorithm.- Reinforcement learning.
「Nielsen BookData」 より