عنوان

Neural networks and statistical learning /

(Number (ISBN

1447155718

(Number (ISBN

9781447155713

Erroneous ISBN

144715570X

Erroneous ISBN

9781447155706

Number

dltt

Title Proper

Neural networks and statistical learning /

General Material Designation

[Book]

First Statement of Responsibility

Ke-Lin Du, M.N.S. Swamy.

Specific Material Designation and Extent of Item

1 online resource (xxvii, 824 pages)

Text of Note

Includes bibliographical references and index

Text of Note

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

0

Text of Note

Providing 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

Title

Neural networks and statistical learning.

International Standard Book Number

9781447155706

Neural networks (Computer science)

Number

Class number

Book number

Du, K.-L.

Swamy, M. N. S.

Ohio Library and Information Network.

Date of Transaction

20150224074437.0

Cataloguing Rules (Descriptive Conventions))

rda

Electronic name

[Book]

Y