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More copies of this ISBN:
Machine Learning (97 Edition)
by Tom M. Mitchell
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Synopses & Reviews 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. Book News Annotation: An introductory text on primary approaches to machine learning and
the study of computer algorithms that improve automatically through
experience. Introduce basics concepts from statistics, artificial
intelligence, information theory, and other disciplines as need
arises, with balanced coverage of theory and practice, and presents
major algorithms with illustrations of their use. Includes chapter
exercises. Online data sets and implementations of several algorithms
are available on a Web site. No prior background in artificial
intelligence or statistics is assumed. For advanced undergraduates
and graduate students in computer science, engineering, statistics,
and social sciences, as well as software professionals.
Annotation c. Book News, Inc., Portland, OR (booknews.com) 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. Table of Contents Chapter 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.
Product Details
- ISBN:
- 9780070428072
- Author:
- Mitchell, Tom M.
- Publisher:
- McGraw-Hill Science/Engineering/Math
- Author:
- Mitchell Thomas
- Author:
- Mitchell, Thomas M.
- Author:
- Mitchell, Thomas
- Location:
- Boston
- Subject:
- Non-Classifiable
- Subject:
- Computer Science
- Subject:
- Machine learning
- Subject:
- Computer algorithms
- Subject:
- Artificial Intelligence - General
- Subject:
- Learning,Computational Learning Theory,Bayesian Learning,Evaluating Hypotheses,Artificial Neural Networks,Decision Tree Learning,General-to-Specific Ordering,Concept Learning,Machine Learning,Analytical Learning,Inductive Logic Programming,Reinforcement L
- Subject:
- Learning,Computational Learning Theory,Bayesian Learning,Evaluating Hypotheses,Artificial Neural Networks,Decision Tree Learning,General-to-Specific Ordering,Concept Learning,Machine Learning,Analytical Learning,Inductive Logic Programming,Reinforcement L
- Subject:
- Learning,Computational Learning Theory,Bayesian Learning,Evaluating Hypotheses,Artificial Neural Networks,Decision Tree Learning,General-to-Specific Ordering,Concept Learning,Machine Learning,Analytical Learning,Inductive Logic Programming,Reinforcement L
- Subject:
- Learning,Computational Learning Theory,Bayesian Learning,Evaluating Hypotheses,Artificial Neural Networks,Decision Tree Learning,General-to-Specific Ordering,Concept Learning,Machine Learning,Analytical Learning,Inductive Logic Programming,Reinforcement L
- Subject:
- Learning,Computational Learning Theory,Bayesian Learning,Evaluating Hypotheses,Artificial Neural Networks,Decision Tree Learning,General-to-Specific Ordering,Concept Learning,Machine Learning,Analytical Learning,Inductive Logic Programming,Reinforcement L
- Subject:
- Learning,Computational Learning Theory,Bayesian Learning,Evaluating Hypotheses,Artificial Neural Networks,Decision Tree Learning,General-to-Specific Ordering,Concept Learning,Machine Learning,Analytical Learning,Inductive Logic Programming,Reinforcement L
- Subject:
- Learning,Computational Learning Theory,Bayesian Learning,Evaluating Hypotheses,Artificial Neural Networks,Decision Tree Learning,General-to-Specific Ordering,Concept Learning,Machine Learning,Analytical Learning,Inductive Logic Programming,Reinforcement L
- Subject:
- Learning,Computational Learning Theory,Bayesian Learning,Evaluating Hypotheses,Artificial Neural Networks,Decision Tree Learning,General-to-Specific Ordering,Concept Learning,Machine Learning,Analytical Learning,Inductive Logic Programming,Reinforcement L
- Subject:
- Learning,Computational Learning Theory,Bayesian Learning,Evaluating Hypotheses,Artificial Neural Networks,Decision Tree Learning,General-to-Specific Ordering,Concept Learning,Machine Learning,Analytical Learning,Inductive Logic Programming,Reinforcement L
- Subject:
- Learning,Computational Learning Theory,Bayesian Learning,Evaluating Hypotheses,Artificial Neural Networks,Decision Tree Learning,General-to-Specific Ordering,Concept Learning,Machine Learning,Analytical Learning,Inductive Logic Programming,Reinforcement L
- Subject:
- Learning,Computational Learning Theory,Bayesian Learning,Evaluating Hypotheses,Artificial Neural Networks,Decision Tree Learning,General-to-Specific Ordering,Concept Learning,Machine Learning,Analytical Learning,Inductive Logic Programming,Reinforcement L
- Subject:
- Intelligence (AI) & Semantics
- Copyright:
- 1997
- Edition Number:
- 1
- Edition Description:
- Includes bibliographical references and indexes.
- Series:
- McGraw-Hill series in computer science
- Series Volume:
- no. 97-1256-HWTR
- Publication Date:
- March 1997
- Binding:
- Hardcover
- Grade Level:
- College/higher education:
- Language:
- English
- Illustrations:
- Yes
- Pages:
- 432
- Dimensions:
- 9.46x6.56x.85 in. 1.56 lbs.
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