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Neural networks and statistical learning

Du, Ke-Lin.

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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 learning/ by 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.
內容註:	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.
Contained By:	Springer eBooks
標題:	Pattern perception. -
電子資源:	https://doi.org/10.1007/978-1-4471-7452-3
ISBN:	9781447174523

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

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

649387
Pattern perception.

LC Class. No.: Q342 / .D855 2019

Dewey Class. No.: 006.3

Neural networks and statistical learning
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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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