Neural Networks and Statistical Learning
K.-L. Du (Author), M. N. S Swamy (Author)
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
1 online resource (XXVII, 824 pages 166 illustrations, 68 illustrations in color.) : online resource
9781447155713, 1447155718
1058081994
Printed edition
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
English
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