Danilo Comminiello is a Tenure-Track Assistant Professor with the Department of Information Engineering, Electronics and Telecommunications (DIET) at Sapienza University of Rome, Italy, where he teaches Machine Learning for Signal Processing. His current research interests include computational intelligence and machine learning theory, particularly focused on audio and acoustic applications. Danilo Comminiello is a Senior Member of “Institute of Electrical and Electronics Engineers” (IEEE), and Member of “Audio Engineering Society” (AES) and “European Association for Signal Processing” (EURASIP). He is also a member of the “Task Force on Computational Audio Processing” of the IEEE “Intelligent System Applications” Technical Committee (IEEE Computational Intelligence Society). Jose C. Principe is a Distinguished Professor of Electrical and Computer Engineering and Biomedical Engineering at the University of Florida where he teaches advanced signal processing, machine learning and artificial neural networks (ANNs) modeling. He is BellSouth Professor and the Founding Director of the University of Florida Computational NeuroEngineering Laboratory (CNEL). His primary research interests are in advanced signal processing with information theoretic criteria (entropy and mutual information) and adaptive models in reproducing kernel Hilbert spaces (RKHS), and the application of these advanced algorithms to Brain Machine Interfaces (BMI). Dr. Principe is a Fellow of the IEEE, ABME, and AIBME. He is the past Editor in Chief of the IEEE Transactions on Biomedical Engineering, past Chair of the Technical Committee on Neural Networks of the IEEE Signal Processing Society, and Past-President of the International Neural Network Society. He received the IEEE EMBS Career Award, and the IEEE Neural Network Pioneer Award. He has more than 600 publications and 30 patents (awarded or filed).

"This book is a joint work of an excellent international team of scientists working in the field of nonlinear signal processing and, in particular, designing adaptive filtering algorithms utilized in system identification and nonlinear system modeling."--Mathematical Reviews Clippings

• 1. IntroductionPART I – LINEAR-IN-THE-PARAMETERS NONLINEAR FILTERS2. Orthogonal LIP Nonlinear Filters3. Spline Adaptive Filters: Theory and Applications4. Recent Advances on LIP Nonlinear Filters and Their Applications: Efficient Solutions and Significance Aware FilteringPART II – ADAPTIVE ALGORITHMS IN THE REPRODUCING KERNEL HILBERT SPACE5. Maximum Correntropy Criterion Based Kernel Adaptive Filters6. Kernel Subspace Learning for Pattern Classification7. A Random Fourier Features Perspective of KAFs with Application to Distributed Learning over Networks8. Kernel-based Inference of Functions over GraphsPART III – NONLINEAR MODELING WITH MULTIPLE LEARNING MACHINES9. Online Nonlinear Modeling via Self-Organizing Trees10. Adaptation and Learning Over Networks for Nonlinear System Modeling11. Cooperative Filtering Architectures for Complex Nonlinear SystemsPART IV – NONLINEAR MODELING BY NEURAL NETWORKS12. Echo State Networks for Multidimensional Data: Exploiting Noncircularity and Widely Linear Models13. Identification of Short-Term and Long-Term Functional Synaptic Plasticity from Spiking Activities14. Adaptive H∞ Tracking Control of Nonlinear Systems using Reinforcement Learning15. Adaptive Dynamic Programming for Optimal Control of Nonlinear Distributed Parameter Systems

Danilo Comminiello is a Tenure-Track Assistant Professor with the Department of Information Engineering, Electronics and Telecommunications (DIET) at Sapienza University of Rome, Italy, where he teaches Machine Learning for Signal Processing. His current research interests include computational intelligence and machine learning theory, particularly focused on audio and acoustic applications. Danilo Comminiello is a Senior Member of “Institute of Electrical and Electronics Engineers” (IEEE), and Member of “Audio Engineering Society” (AES) and “European Association for Signal Processing” (EURASIP). He is also a member of the “Task Force on Computational Audio Processing” of the IEEE “Intelligent System Applications” Technical Committee (IEEE Computational Intelligence Society). Jose C. Principe is a Distinguished Professor of Electrical and Computer Engineering and Biomedical Engineering at the University of Florida where he teaches advanced signal processing, machine learning and artificial neural networks (ANNs) modeling. He is BellSouth Professor and the Founding Director of the University of Florida Computational NeuroEngineering Laboratory (CNEL). His primary research interests are in advanced signal processing with information theoretic criteria (entropy and mutual information) and adaptive models in reproducing kernel Hilbert spaces (RKHS), and the application of these advanced algorithms to Brain Machine Interfaces (BMI). Dr. Principe is a Fellow of the IEEE, ABME, and AIBME. He is the past Editor in Chief of the IEEE Transactions on Biomedical Engineering, past Chair of the Technical Committee on Neural Networks of the IEEE Signal Processing Society, and Past-President of the International Neural Network Society. He received the IEEE EMBS Career Award, and the IEEE Neural Network Pioneer Award. He has more than 600 publications and 30 patents (awarded or filed).

"This book is a joint work of an excellent international team of scientists working in the field of nonlinear signal processing and, in particular, designing adaptive filtering algorithms utilized in system identification and nonlinear system modeling."--Mathematical Reviews Clippings

• 1. IntroductionPART I – LINEAR-IN-THE-PARAMETERS NONLINEAR FILTERS2. Orthogonal LIP Nonlinear Filters3. Spline Adaptive Filters: Theory and Applications4. Recent Advances on LIP Nonlinear Filters and Their Applications: Efficient Solutions and Significance Aware FilteringPART II – ADAPTIVE ALGORITHMS IN THE REPRODUCING KERNEL HILBERT SPACE5. Maximum Correntropy Criterion Based Kernel Adaptive Filters6. Kernel Subspace Learning for Pattern Classification7. A Random Fourier Features Perspective of KAFs with Application to Distributed Learning over Networks8. Kernel-based Inference of Functions over GraphsPART III – NONLINEAR MODELING WITH MULTIPLE LEARNING MACHINES9. Online Nonlinear Modeling via Self-Organizing Trees10. Adaptation and Learning Over Networks for Nonlinear System Modeling11. Cooperative Filtering Architectures for Complex Nonlinear SystemsPART IV – NONLINEAR MODELING BY NEURAL NETWORKS12. Echo State Networks for Multidimensional Data: Exploiting Noncircularity and Widely Linear Models13. Identification of Short-Term and Long-Term Functional Synaptic Plasticity from Spiking Activities14. Adaptive H∞ Tracking Control of Nonlinear Systems using Reinforcement Learning15. Adaptive Dynamic Programming for Optimal Control of Nonlinear Distributed Parameter Systems

Danilo Comminiello is a Tenure-Track Assistant Professor with the Department of Information Engineering, Electronics and Telecommunications (DIET) at Sapienza University of Rome, Italy, where he teaches Machine Learning for Signal Processing. His current research interests include computational intelligence and machine learning theory, particularly focused on audio and acoustic applications. Danilo Comminiello is a Senior Member of “Institute of Electrical and Electronics Engineers” (IEEE), and Member of “Audio Engineering Society” (AES) and “European Association for Signal Processing” (EURASIP). He is also a member of the “Task Force on Computational Audio Processing” of the IEEE “Intelligent System Applications” Technical Committee (IEEE Computational Intelligence Society). Jose C. Principe is a Distinguished Professor of Electrical and Computer Engineering and Biomedical Engineering at the University of Florida where he teaches advanced signal processing, machine learning and artificial neural networks (ANNs) modeling. He is BellSouth Professor and the Founding Director of the University of Florida Computational NeuroEngineering Laboratory (CNEL). His primary research interests are in advanced signal processing with information theoretic criteria (entropy and mutual information) and adaptive models in reproducing kernel Hilbert spaces (RKHS), and the application of these advanced algorithms to Brain Machine Interfaces (BMI). Dr. Principe is a Fellow of the IEEE, ABME, and AIBME. He is the past Editor in Chief of the IEEE Transactions on Biomedical Engineering, past Chair of the Technical Committee on Neural Networks of the IEEE Signal Processing Society, and Past-President of the International Neural Network Society. He received the IEEE EMBS Career Award, and the IEEE Neural Network Pioneer Award. He has more than 600 publications and 30 patents (awarded or filed).

"This book is a joint work of an excellent international team of scientists working in the field of nonlinear signal processing and, in particular, designing adaptive filtering algorithms utilized in system identification and nonlinear system modeling."--Mathematical Reviews Clippings

• 1. IntroductionPART I – LINEAR-IN-THE-PARAMETERS NONLINEAR FILTERS2. Orthogonal LIP Nonlinear Filters3. Spline Adaptive Filters: Theory and Applications4. Recent Advances on LIP Nonlinear Filters and Their Applications: Efficient Solutions and Significance Aware FilteringPART II – ADAPTIVE ALGORITHMS IN THE REPRODUCING KERNEL HILBERT SPACE5. Maximum Correntropy Criterion Based Kernel Adaptive Filters6. Kernel Subspace Learning for Pattern Classification7. A Random Fourier Features Perspective of KAFs with Application to Distributed Learning over Networks8. Kernel-based Inference of Functions over GraphsPART III – NONLINEAR MODELING WITH MULTIPLE LEARNING MACHINES9. Online Nonlinear Modeling via Self-Organizing Trees10. Adaptation and Learning Over Networks for Nonlinear System Modeling11. Cooperative Filtering Architectures for Complex Nonlinear SystemsPART IV – NONLINEAR MODELING BY NEURAL NETWORKS12. Echo State Networks for Multidimensional Data: Exploiting Noncircularity and Widely Linear Models13. Identification of Short-Term and Long-Term Functional Synaptic Plasticity from Spiking Activities14. Adaptive H∞ Tracking Control of Nonlinear Systems using Reinforcement Learning15. Adaptive Dynamic Programming for Optimal Control of Nonlinear Distributed Parameter Systems