Machine learning / Tom M. Mitchell, Carnegie Mellon University.

By: Mitchell, Tom M. (Tom Michael), 1951- [author.]Material type: TextTextLanguage: English Series: McGraw-Hill series in computer scienceNew York : McGraw-Hill, [1997]©1997 Description: xvii, 414 pages : illustrations ; 25 cmContent type: text Media type: unmediated Carrier type: volumeISBN: 9780071154673 (paperback)Subject(s): Machine learning | Computer algorithmsLOC classification: Q325.5 .M58 1997
Contents:
1. Introduction -- 2. Concept Learning and the 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
Summary: Mitchell covers the field of machine learning, the study of algorithms that allow computer programs to automatically improve through experience and that automatically infer general laws from specific data
Item type Current library Shelving location Call number Copy number Status Date due Barcode
Books MEF Üniversitesi Kütüphanesi
Genel Koleksiyon Q 325.5 .M58 1997 (Browse shelf (Opens below)) Available 0011385

Includes bibliographical references and indexes.

1. Introduction -- 2. Concept Learning and the 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

Mitchell covers the field of machine learning, the study of algorithms that allow computer programs to automatically improve through experience and that automatically infer general laws from specific data