000 04096nam a22005055i 4500
001 978-1-4471-5571-3
003 DE-He213
005 20200421112553.0
007 cr nn 008mamaa
008 131206s2014 xxk| s |||| 0|eng d
020 _a9781447155713
_9978-1-4471-5571-3
024 7 _a10.1007/978-1-4471-5571-3
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aDu, Ke-Lin.
_eauthor.
245 1 0 _aNeural Networks and Statistical Learning
_h[electronic resource] /
_cby Ke-Lin Du, M. N. S. Swamy.
264 1 _aLondon :
_bSpringer London :
_bImprint: Springer,
_c2014.
300 _aXXVII, 824 p. 166 illus., 68 illus. in color.
_bonline resource.
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.
650 0 _aData mining.
650 0 _aPattern recognition.
650 0 _aNeural networks (Computer science).
650 0 _aComputational intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aPattern Recognition.
700 1 _aSwamy, M. N. S.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781447155706
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4471-5571-3
912 _aZDB-2-ENG
942 _cEBK
999 _c58981
_d58981