Mitchell, Tom M.  1951-, <br/><br/>
Machine learning / 
Tom M. Mitchell, Carnegie Mellon University. 
 - xvii, 414 pages :  illustrations ;  25 cm. 
 - McGraw-Hill series in computer science .
 - McGraw-Hill series in computer science .
<br/><br/>Includes bibliographical references and indexes. 
<br/><br/>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 
<br/><br/> 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 
<br/><br/><label>ISBN: </label>9780071154673 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Machine learning<br/>Computer algorithms
<br/><br/><label>LC Class. No.: </label>Q325.5  / .M58 1997