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Machine Learning

作者：Tom M. Mitchell
原價：NT$ 1,100

ISBN：9780071154673
版次：1
年份：1997
出版商：McGraw-Hill
頁數/規格：414頁/平裝單色

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內容介紹本書特色目錄作者介紹

Description
Machine Learning is the study of computer algorithms that improve automatically through experience. Successful applications range from data mining programs that discover general rules from large databases, to information filtering systems that learn users' reading preferences, to autonomous vehicles that learn to drive on public highways.

Machine Learning is an inherently interdisciplinary field, build on concepts from artificial intelligence, probability and statistics, information theory, philosophy, control theory, psychology, neurobiology, and other fields.

This textbook provides a single source introduction to the primary approaches to machine learning. It is intended for advanced undergraduate and graduate students, as well as for developers and researchers in the field. No prior background in artificial intelligence or statistics is assumed.

Several key algorithms, example date sets, and project-oriented homework assignments discussed in the book are accessible through the World Wide Web.

Features

No other book covers the concepts and techniques from the various fields in a unified fashion.

Covers very recent subjects such as genetic algorithms, reinforcement learning, and inductive logic programming.

Writing style is clear, explanatory and precise.

Table of Contents
Chapter 1. Introduction
Chapter 2. Concept Learning and the General-to-Specific Ordering
Chapter 3. Decision Tree Learning
Chapter 4. Artificial Neural Networks
Chapter 5. Evaluating Hypotheses
Chapter 6. Bayesian Learning
Chapter 7. Computational Learning Theory
Chapter 8. Instance-Based Learning
Chapter 9. Genetic Algorithms
Chapter 10. Learning Sets of Rules
Chapter 11. Analytical Learning
Chapter 12. Combining Inductive and Analytical Learning
Chapter 13. Reinforcement Learning

Tom M. Mitchell, Carnegie Mellon University

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