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Neural networks and statistical learning

Du, Ke-Lin.

Neural networks and statistical learning

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Neural networks and statistical learningby Ke-Lin Du, M. N. S. Swamy.
作者:	Du, Ke-Lin.
其他作者:	Swamy, M. N. S.
出版者:	London :Springer London :2019.
面頁冊數:	xxx, 988 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
標題:	Pattern perception.
電子資源:	https://doi.org/10.1007/978-1-4471-7452-3
ISBN:	9781447174523$q(electronic bk.)

Neural networks and statistical learning
Du, Ke-Lin.

Neural networks and statistical learning[electronic resource] /by Ke-Lin Du, M. N. S. Swamy. - 2nd ed. - London :Springer London :2019. - xxx, 988 p. :ill. (some col.), digital ;24 cm.

Introduction -- 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.

ISBN: 9781447174523$q(electronic bk.)

Standard No.: 10.1007/978-1-4471-7452-3doiSubjects--Topical Terms:

182522
Pattern perception.

LC Class. No.: Q342 / .D855 2019

Dewey Class. No.: 006.3

Neural networks and statistical learning
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245 1 0 $a Neural networks and statistical learning $h [electronic resource] / $c by Ke-Lin Du, M. N. S. Swamy.
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260 $a London : $b Springer London : $b Imprint: Springer, $c 2019.
300 $a xxx, 988 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a Introduction -- 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.
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650 0 $a Computational intelligence. $3 210824
650 0 $a Neural networks (Computer science) $3 181982
650 0 $a Engineering. $3 210888
650 0 $a Data mining. $3 184440
650 0 $a Optical pattern recognition. $3 188326
650 1 4 $a Mathematical Models of Cognitive Processes and Neural Networks. $3 567118
650 2 4 $a Computational Intelligence. $3 338479
650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Pattern Recognition. $3 273706
650 2 4 $a Signal, Image and Speech Processing. $3 273768
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