Machine Learning

  • Authors:
  • Thomas M. Mitchell

  • Affiliations:
  • -

  • Venue:
  • Machine Learning
  • Year:
  • 1997

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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 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.