000			02002cam a22003015a 4500
001			ssj0001558340
003			OSt
005			20201128023735.0
008			150715t2015 gw a frb 001 0 eng d
020			_a9783319200095
040			_dWaSeSS _dEG-ScBUE _beng
082	0	4	_a006.3 _222 _bKUB
100	1		_aKubat, Miroslav, _939937 _d1958-
245	1	3	_aAn Introduction to Machine Learning / _cMiroslav Kubat.
260			_aCham : _bSpringer, _cc.2015.
300			_axiii, 291 p. : _bill. ; _c24 cm.
500			_aIndex : p. 291.
504			_aBibliography : p. 287-290.
505	0		_aA Simple Machine-Learning Task -- Probabilities: Bayesian Classifiers -- Similarities: Nearest-Neighbor Classifiers -- Inter-Class Boundaries: Linear and Polynomial Classifiers -- Artificial Neural Networks -- Decision Trees -- Computational Learning Theory -- A Few Instructive Applications -- Induction of Voting Assemblies -- Some Practical Aspects to Know About -- Performance Evaluation.-Statistical Significance -- The Genetic Algorithm -- Reinforcement learning.
506			_aAvailable on campus and off campus with authorized login.
520			_aThis book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of “boosting, ” how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms.
650		7	_aMachine learning. _2BUEsh _92922
653			_bCOMSCI _cMay2016
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783319200095
942			_2ddc
999			_c21712 _d21684