Neural Networks and Statistical Learning

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Springer Science & Business Media, Dec 9, 2013 - Technology & Engineering - 824 pages

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.

 

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Contents

1 Introduction
1
2 Fundamentals of Machine Learning
15
3 Perceptrons
67
Architecture and Error Backpropagation
82
Other Learning Techniques
127
6 Hopfield Networks Simulated Annealing and Chaotic Neural Networks
159
7 Associative Memory Networks
187
Basic Clustering Models and Algorithms
215
16 Support Vector Machines
469
17 Other Kernel Methods
525
18 Reinforcement Learning
547
19 Probabilistic and Bayesian Networks
562
Data Fusion and Emsemble Learning
621
21 Introduction to Fuzzy Sets and Logic
644
22 Neurofuzzy Systems
677
23 Neural Circuits and Parallel Implementation
705

Topics in Clustering
259
10 Radial Basis Function Networks
298
11 Recurrent Neural Networks
337
12 Principal Component Analysis
354
13 Nonnegative Matrix Factorization
407
14 Independent Component Analysis
418
15 Discriminant Analysis
451
24 Pattern Recognition for Biometrics and Bioinformatics
727
25 Data Mining
746
A Mathematical Preliminaries
779
B Benchmarks and Resources
799
About the Authors
813
Index
815
Copyright

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About the author (2013)

Ke-Lin Du is currently the Chief Scientist at Enjoyor Inc., China. He is also an Affiliate Associate Professor in Department of Electrical and Computer Engineering at Concordia University, Canada. Prior to joining Enjoyor Inc. in 2012, he held positions with Huawei Technologies, the China Academy of Telecommunication Technology, the Chinese University of Hong Kong, the Hong Kong University of Science and Technology, and Concordia University. He has published two books and over 50 papers, and filed over 15 patents. His current research interests include signal processing, neural networks, intelligent systems, and wireless communications. He is a Senior Member of the IEEE.M.N.S. Swamy is currently a Research Professor and holder of the Concordia Tier I Research Chair Signal Processing in the Department of Electrical and Computer Engineering, Concordia University, where he was Dean of the Faculty of Engineering and Computer Science from 1977 to 1993 and the founding Chair of the EE department. He has published extensively in the areas of circuits, systems and signal processing, and co-authored five books. Professor Swamy is a Fellow of the IEEE, IET (UK) and EIC (Canada), and has received many IEEE-CAS awards, including the Guillemin-Cauer award in 1986, as well as the Education Award and the Golden Jubilee Medal, both in 2000.