Machine learning

1 December 1996

journal article
Published by Association for Computing Machinery (ACM) in ACM Computing Surveys

Vol. 28 (4es) , 3
https://doi.org/10.1145/242224.242229

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 contentsChapter 1. IntroductionChapter 2. Concept Learning and the General-to-Specific OrderingChapter 3. Decision Tree LearningChapter 4. Artificial Neural NetworksChapter 5. Evaluating HypothesesChapter 6. Bayesian LearningChapter 7. Computational Learning TheoryChapter 8. Instance-Based LearningChapter 9. Inductive Logic ProgrammingChapter 10. Analytical LearningChapter 11. Combining Inductive and Analytical LearningChapter 12. Reinforcement Learning.

Keywords

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