000			01657 am a2200349 i 4500
001			15059
005			20260106102029.0
008			970211s1997 nyua b 001 0 eng
020			_a0070428077
035			_a(OCoLC)
040			_aBAUN _beng _cBAUN _erda
041	0		_aeng
049			_aBAUN_MERKEZ
050	0	4	_aQ325.5 _b.M58 1997
100	1		_aMitchell, Tom M. (Tom Michael), _d1951- _991572 _eaut
245	1	0	_aMachine learning / _cTom M. Mitchell
264		1	_aNew York: _bThe McGraw-Hill, _c1997,©1997
264		4	_c©1997
300			_axvii,431 pagess : _billustrations ; _c25 cm
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
490	1		_aMcGraw-Hill series in computer science
504			_aIncludes bibliographical references and index.
505	0	0	_t1. Introduction _t--2. Concept Learning and the General-to-Specific Ordering _t--3. Decision Tree Learning _t--4. Artificial Neural Networks _t--5. Evaluating Hypotheses _t--6. Bayesian Learning _t--7. Computational Learning Theory _t--8. Instance-Based Learning _t--9. Genetic Algorithms _t--10. Learning Sets of Rules _t--11. Analytical Learning _t--12. Combining Inductive and Analytical Learning _t--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		0	_aComputer algorithms _9331
650		0	_aMachine learning. _96268
830		0	_aMcGraw-Hill series in computer science.
900			_bsatın
942			_2lcc _cKT
999			_c12853 _d12853