Simon Haykin is University Professor at McMaster University, Hamilton, Ontario, Canada. His research interests include nonlinear dynamics, neural networks and adaptive filters and their applications in radar and communications systems. Dr. Haykin is the editor for a series of books on Adaptive and Learning Systems for Signal Processing, Communications and Control published by John Wiley & Sons, Inc. He is both an IEEE Fellow and Fellow of the Royal Society of Canada. Bart Kosko is a past director of the University of Southern California’s (USC) Signal and Image Processing Institute. He has authored several books, including Neural Networks and Fuzzy Systems, Neural Networks for Signal Processing (Prentice Hall, 1992) Fuzzy Engineering (Prentice Hall, 1997) and Fuzzy Thinking (Hyperion, 1993), as well as the novel Nanotime (Avon Books, 1997) and Heaven in a Chip (Random House, 2000). Dr. Kosko is an elected governor of the International Neural Network Society and has chaired many neural and fuzzy system conferences. He is a faculty member of electrical engineering at USC.

• Preface. List of Contributors. Humanistic Intelligence: "Wear Comp" As a New Framework and Application for Intelligent Signal Processing. Adaptive Stochastic Resonance. Learning in the Presence of Noise. Incorporating Prior Information in Machine Learning by Creating Virtual Examples. Deterministic Annealing for Clustering, Compression, Classification, Regression, and Speech recognition. Local Dynamic Modeling with Self-Organizing Maps and Applications to Nonlinear System Identification and Control. A Signal Processing Framework Based on Dynamic Neural Networks with Application to Problems in Adaptation, Filtering and Classification. Semiparametric Support Vector Machines for Nonlinear Model Estimation. Gradient-Based Learning Applied to Document Recognition. Pattern Recognition Using A Family of Design Algorithms Based Upon Generalized Probabilistic Descent Method. An Approach to Adaptive Classification. Reduced-Rank Intelligent Signal Processing with Application to Radar. Signal Detection in a Nonstationary Environment Reformulated as an Adaptive Pattern Classification Problem. Data Representation Using Mixtures of Principal Components. Image Denoising by Sparse Code Shrinkage. Index. About the Editors.

Simon Haykin is University Professor at McMaster University, Hamilton, Ontario, Canada. His research interests include nonlinear dynamics, neural networks and adaptive filters and their applications in radar and communications systems. Dr. Haykin is the editor for a series of books on Adaptive and Learning Systems for Signal Processing, Communications and Control published by John Wiley & Sons, Inc. He is both an IEEE Fellow and Fellow of the Royal Society of Canada. Bart Kosko is a past director of the University of Southern California’s (USC) Signal and Image Processing Institute. He has authored several books, including Neural Networks and Fuzzy Systems, Neural Networks for Signal Processing (Prentice Hall, 1992) Fuzzy Engineering (Prentice Hall, 1997) and Fuzzy Thinking (Hyperion, 1993), as well as the novel Nanotime (Avon Books, 1997) and Heaven in a Chip (Random House, 2000). Dr. Kosko is an elected governor of the International Neural Network Society and has chaired many neural and fuzzy system conferences. He is a faculty member of electrical engineering at USC.

• Preface. List of Contributors. Humanistic Intelligence: "Wear Comp" As a New Framework and Application for Intelligent Signal Processing. Adaptive Stochastic Resonance. Learning in the Presence of Noise. Incorporating Prior Information in Machine Learning by Creating Virtual Examples. Deterministic Annealing for Clustering, Compression, Classification, Regression, and Speech recognition. Local Dynamic Modeling with Self-Organizing Maps and Applications to Nonlinear System Identification and Control. A Signal Processing Framework Based on Dynamic Neural Networks with Application to Problems in Adaptation, Filtering and Classification. Semiparametric Support Vector Machines for Nonlinear Model Estimation. Gradient-Based Learning Applied to Document Recognition. Pattern Recognition Using A Family of Design Algorithms Based Upon Generalized Probabilistic Descent Method. An Approach to Adaptive Classification. Reduced-Rank Intelligent Signal Processing with Application to Radar. Signal Detection in a Nonstationary Environment Reformulated as an Adaptive Pattern Classification Problem. Data Representation Using Mixtures of Principal Components. Image Denoising by Sparse Code Shrinkage. Index. About the Editors.

Simon Haykin is University Professor at McMaster University, Hamilton, Ontario, Canada. His research interests include nonlinear dynamics, neural networks and adaptive filters and their applications in radar and communications systems. Dr. Haykin is the editor for a series of books on Adaptive and Learning Systems for Signal Processing, Communications and Control published by John Wiley & Sons, Inc. He is both an IEEE Fellow and Fellow of the Royal Society of Canada. Bart Kosko is a past director of the University of Southern California’s (USC) Signal and Image Processing Institute. He has authored several books, including Neural Networks and Fuzzy Systems, Neural Networks for Signal Processing (Prentice Hall, 1992) Fuzzy Engineering (Prentice Hall, 1997) and Fuzzy Thinking (Hyperion, 1993), as well as the novel Nanotime (Avon Books, 1997) and Heaven in a Chip (Random House, 2000). Dr. Kosko is an elected governor of the International Neural Network Society and has chaired many neural and fuzzy system conferences. He is a faculty member of electrical engineering at USC.

• Preface. List of Contributors. Humanistic Intelligence: "Wear Comp" As a New Framework and Application for Intelligent Signal Processing. Adaptive Stochastic Resonance. Learning in the Presence of Noise. Incorporating Prior Information in Machine Learning by Creating Virtual Examples. Deterministic Annealing for Clustering, Compression, Classification, Regression, and Speech recognition. Local Dynamic Modeling with Self-Organizing Maps and Applications to Nonlinear System Identification and Control. A Signal Processing Framework Based on Dynamic Neural Networks with Application to Problems in Adaptation, Filtering and Classification. Semiparametric Support Vector Machines for Nonlinear Model Estimation. Gradient-Based Learning Applied to Document Recognition. Pattern Recognition Using A Family of Design Algorithms Based Upon Generalized Probabilistic Descent Method. An Approach to Adaptive Classification. Reduced-Rank Intelligent Signal Processing with Application to Radar. Signal Detection in a Nonstationary Environment Reformulated as an Adaptive Pattern Classification Problem. Data Representation Using Mixtures of Principal Components. Image Denoising by Sparse Code Shrinkage. Index. About the Editors.