000			03866Cam#a22004455i#4500
001			INGC-EBK-000032
003			AR-LpUFI
005			20220927105559.0
007			cr nn 008mamaa
008			131206s2014 xxk| s |||| 0|eng d
020			_a9781447155713
024	7		_a10.1007/978-1-4471-5571-3 _2doi
050		4	_aQ342
072		7	_aUYQ _2bicssc
072		7	_aCOM004000 _2bisacsh
100	1		_aDu, Ke-Lin. _9259836
245	1	0	_aNeural Networks and Statistical Learning _h[libro electrónico] / _cby Ke-Lin Du, M. N. S. Swamy.
260		1	_aLondon : _bSpringer London : _bImprint: Springer, _c2014.
300			_axxvii, 824 p. : _bil.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
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.
520			_aProviding a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included. Focusing on the prominent accomplishments and their practical aspects, academic and technical staff, graduate students and researchers will find that this provides a solid foundation and encompassing reference for the fields of neural networks, pattern recognition, signal processing, machine learning, computational intelligence, and data mining.
650		0	_aEngineering. _9259622
650		0	_aData mining. _9259837
650		0	_aPattern recognition. _9259838
650		0	_aNeural networks (Computer science). _9259839
650		0	_aComputational intelligence. _9259845
650	2	4	_aMathematical Models of Cognitive Processes _9259841
650	2	4	_aKnowledge Discovery. _9259842
700	1		_aSwamy, M. N. S. _9259843
776	0	8	_iPrinted edition: _z9781447155706
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4471-5571-3
912			_aZDB-2-ENG
929			_aCOM
942			_cEBK
999			_aGEB _c27460 _d27460