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Machine Learning, 1 edition
Machine Learning, 1 edition
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Source
Pages: 432  
Medium: Hardback
Year of Publication: 1997
ISBN:9780070428072
Author
Publisher
McGraw-Hill, Inc.  New York, NY, USA
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ABSTRACT

This exciting addition to the McGraw-Hill Series in Computer Science focuses on the concepts and techniques that contribute to the rapidly changing field of machine learning--including probability and statistics, artificial intelligence, and neural networks--unifying them all in a logical and coherent manner. Machine Learning serves as a useful reference tool for software developers and researchers, as well as an outstanding text for college students.
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. Inductive Logic Programming
Chapter 10. Analytical Learning
Chapter 11. Combining Inductive and Analytical Learning
Chapter 12. Reinforcement Learning.


CITED BY  1,198