Items related to Machine Learning (McGraw-Hill Series in Computer Science)
Synopsis
This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. The book is intended to support upper level undergraduate and introductory level graduate courses in machine learning.
"synopsis" may belong to another edition of this title.
From the Publisher
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.
From the Author
Table of Contents:
1. Introduction
2. Concept Learning and General-to-Specific Ordering
3. Decision Tree Learning
4. Artificial Neural Networks
5. Evaluating Hypotheses
6. Bayesian Learning
7. Computational Learning Theory
8. Instance-Based Learning
9. Genetic Algorithms
10. Learning Sets of Rules
11. Analytical Learning
12. Combining Inductive and Analytical Learning
13. Reinforcement Learning
Includes web-accessible data and code.
"About this title" may belong to another edition of this title.
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Machine Learning
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McGraw-Hill Education (edition 1), 1997
ISBN 10: 0070428077
ISBN 13: 9780070428072
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Machine Learning
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Machine Learning (McGraw-Hill Series in Computer Science)
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Machine Learning
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Machine Learning
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Machine Learning
Published by
McGraw-Hill Education, 1997
ISBN 10: 0070428077
ISBN 13: 9780070428072
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First Edition
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Machine Learning
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