Machine Learning: An Artificial Intelligence Approach: An Artifical Intelligence Approach (Symbolic Computation) - Hardcover

Hardcover

ISBN 10: 3540132988 ISBN 13: 9783540132981

Publisher: Springer, 1984

Synopsis

The ability to learn is one of the most fundamental attributes of intelligent behavior. Consequently, progress in the theory and computer modeling of learn­ ing processes is of great significance to fields concerned with understanding in­ telligence. Such fields include cognitive science, artificial intelligence, infor­ mation science, pattern recognition, psychology, education, epistemology, philosophy, and related disciplines. The recent observance of the silver anniversary of artificial intelligence has been heralded by a surge of interest in machine learning-both in building models of human learning and in understanding how machines might be endowed with the ability to learn. This renewed interest has spawned many new research projects and resulted in an increase in related scientific activities. In the summer of 1980, the First Machine Learning Workshop was held at Carnegie-Mellon University in Pittsburgh. In the same year, three consecutive issues of the Inter­ national Journal of Policy Analysis and Information Systems were specially devoted to machine learning (No. 2, 3 and 4, 1980). In the spring of 1981, a special issue of the SIGART Newsletter No. 76 reviewed current research projects in the field. . This book contains tutorial overviews and research papers representative of contemporary trends in the area of machine learning as viewed from an artificial intelligence perspective. As the first available text on this subject, it is intended to fulfill several needs.

About 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 edition

• Publisher: Springer
• Publication date: 1984
• ISBN 10: 3540132988
• ISBN 13: 9783540132981
• Binding: Hardcover
• Number of pages: 572
• Editor: Michalski R.S., Carbonell J.G., Mitchell T.M.