000			02197cam a22003135i 4500
999			_c7807 _d7807
001			17865123
005			20210506103913.0
008			130826s2014 enk 000 0 eng
010			_a 2013948860
020			_a9781447155706
040			_aDLC _cDLC _erda _dDLC _dEG-NcFUE
082	0	4	_223 _a006.32 _bD.K.N
100	1		_aDu, Ke-Lin‏ _926401 _d1971-
245	1	0	_aNeural networks and statistical learning / _cby Ke-Lin Du, M.N.S. Swamy.
264		1	_aLondon : _bSpringer, _c2014.
300			_a1 volume : _b illustrations (black and white, and colour) ; _c24 cm
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
505	0		_aIntroduction.- Fundamentals of Machine Learning.- Perceptrons.- Multilayer perceptrons: architecture and error backpropagation.- Multilayer perceptrons: other learing techniques.- Hopfield networks, simulated annealing and chaotic neural networks.- Associative memory networks.- Clustering I: Basic clustering models and algorithms.- Clustering II: topics in clustering.- Radial basis function networks.- Recurrent neural networks.- Principal component analysis.- Nonnegative matrix factorization and compressed sensing.- Independent component analysis.- Discriminant analysis.- Support vector machines.- Other kernel methods.- Reinforcement learning.- Probabilistic and Bayesian networks.- Combining multiple learners: data fusion and emsemble learning.- Introduction of fuzzy sets and logic.- Neurofuzzy systems.- Neural circuits.- Pattern recognition for biometrics and bioinformatics.- Data mining.- Appenidx A. Mathematical Preliminaries.- Appendix B. Benchmarks and resources.
650		0	_aNeural networks (Computer science)
700	1		_aSwamy, M. N. S.,
856	4	2	_3Contributor biographical information _uhttp://www.loc.gov/catdir/enhancements/fy1317/2013948860-b.html
856	4	2	_3Publisher description _uhttp://www.loc.gov/catdir/enhancements/fy1317/2013948860-d.html
856	4	1	_3Table of contents only _uhttp://www.loc.gov/catdir/enhancements/fy1317/2013948860-t.html
856	4	1	_3Abstract _uhttp://repository.fue.edu.eg/xmlui/handle/123456789/3528
942			_cBK _2ddc