000			02156cam a2200385 i 4500
999			_c10501 _d10501
001			18556845
005			20190424113603.0
008			150407s2015 flua b 001 0 eng
010			_a 2014434325 _z 2015302878
020			_a9781466583283 (hbk)
020			_a1466583282 (hbk)
040			_aDLC _beng _erda _cDLC
042			_apcc
050	0	0	_aQ325.5 _b.M368 2015
082	0	4	_222 _a006.31 _bM.S.M
100	1		_aMarsland, Stephen.
245	1	0	_aMachine learning : _ban algorithmic perspective / _cStephen Marsland.
250			_a2nd. ed.
264		1	_aBoca Raton : _bCRC Press, _c[2015]
300			_axx, 437 pages : _billustrations ; _c25 cm.
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
490	0		_aChapman & Hall/CRC machine learning & pattern recognition series
500			_a"A Chapman & Hall book."
504			_aIncludes bibliographical references and index.
505	0		_aIntroduction -- Preliminaries -- Neurons, neural networks, and linear discriminants -- The multi-layer perceptron -- Radial basis functions and splines -- Dimensionality reduction -- Probabilistic learning -- Support vector machines -- Optimisation and search -- Evolutionary learning -- Reinforcement learning -- Learning with trees -- Decision by committee: ensemble learning -- Unsupervised learning -- Markov chain Monte Carlo (MCMC) methods -- Graphical models -- Symmetric weights and deep belief networks -- Gaussian processes -- Python.
520			_aAnnotation Written in an easily accessible style, this text provides the ideal blend of theory and practical, applicable knowledge. It covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization.
650		0	_aMachine learning.
650		0	_aAlgorithms.
856			_3Abstract _uhttp://repository.fue.edu.eg/xmlui/handle/123456789/3653
906			_a7 _bcbc _corigres _d2 _encip _f20 _gy-gencatlg
942			_2ddc _cBK