000			01273nam a22002417a 4500
999			_c51439 _d52986
003			ISURa
008			190617b xxu||||| |||| 00| 0 eng d
020			_a9781259096952
020			_a1259096955
041			_aEnglish
082			_a006.31 _bMIT
100			_aMitchell, Tom M. _972530
245			_aMachine Learning
260			_aChennai _bMcGraw-Hill, _c1997
300			_axvii, 414 p. _bill. _c25 cm
440			_aMcGraw-Hill series in computer science. _972531
500			_a 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.
520			_aMitchell 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.
650			_aComputer algorithms _95760
650			_aMachine learning _969133
650			_aAlgorithmes. _972534
942			_2ddc _cLN