LEADER 05144nam 22007215 450 001 9910299755103321 005 20251117071851.0 010 $a1-4471-5571-8 024 7 $a10.1007/978-1-4471-5571-3 035 $a(CKB)3710000000078699 035 $a(DE-He213)978-1-4471-5571-3 035 $a(SSID)ssj0001089970 035 $a(PQKBManifestationID)11581019 035 $a(PQKBTitleCode)TC0001089970 035 $a(PQKBWorkID)11126777 035 $a(PQKB)11051140 035 $a(MiAaPQ)EBC6312757 035 $a(MiAaPQ)EBC1591892 035 $a(Au-PeEL)EBL1591892 035 $a(CaPaEBR)ebr10965897 035 $a(OCoLC)869904552 035 $a(PPN)176097171 035 $a(EXLCZ)993710000000078699 100 $a20131206d2014 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNeural Networks and Statistical Learning /$fby Ke-Lin Du, M. N. S. Swamy 205 $a1st ed. 2014. 210 1$aLondon :$cSpringer London :$cImprint: Springer,$d2014. 215 $a1 online resource (XXVII, 824 p. 166 illus., 68 illus. in color.) 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a1-4471-5570-X 327 $aIntroduction -- Fundamentals of Machine Learning -- Perceptrons -- Multilayer perceptrons: architecture and error backpropagation -- Multilayer perceptrons: other learning 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 ensemble learning -- Introduction of fuzzy sets and logic -- Neurofuzzy systems -- Neural circuits -- Pattern recognition for biometrics and bioinformatics -- Data mining. 330 $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. 606 $aComputational intelligence 606 $aNeural networks (Computer science) 606 $aData mining 606 $aPattern perception 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aMathematical Models of Cognitive Processes and Neural Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/M13100 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 615 0$aComputational intelligence. 615 0$aNeural networks (Computer science) 615 0$aData mining. 615 0$aPattern perception. 615 14$aComputational Intelligence. 615 24$aMathematical Models of Cognitive Processes and Neural Networks. 615 24$aData Mining and Knowledge Discovery. 615 24$aPattern Recognition. 676 $a006.32 700 $aDu$b Ke-Lin$4aut$4http://id.loc.gov/vocabulary/relators/aut$0756075 702 $aSwamy$b M. N. S.$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299755103321 996 $aNeural Networks and Statistical Learning$92041918 997 $aUNINA