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作者 Rojo-Alvarez, Jose Luis
書名 Digital Signal Processing with Kernel Methods
出版項 Somerset : John Wiley & Sons, Incorporated, 2018
©2018
國際標準書號 9781118705827 (electronic bk.)
9781118611791
book jacket
說明 1 online resource (668 pages)
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
系列 Wiley - IEEE Ser
Wiley - IEEE Ser
附註 Intro -- Title Page -- Copyright Page -- Contents -- About the Authors -- Preface -- Acknowledgements -- List of Abbreviations -- Part I Fundamentals and Basic Elements -- Chapter 1 From Signal Processing to Machine Learning -- 1.1 A New Science is Born: Signal Processing -- 1.1.1 Signal Processing Before Being Coined -- 1.1.2 1948: Birth of the Information Age -- 1.1.3 1950s: Audio Engineering Catalyzes Signal Processing -- 1.2 From Analog to Digital Signal Processing -- 1.2.1 1960s: Digital Signal Processing Begins -- 1.2.2 1970s: Digital Signal Processing Becomes Popular -- 1.2.3 1980s: Silicon Meets Digital Signal Processing -- 1.3 Digital Signal Processing Meets Machine Learning -- 1.3.1 1990s: New Application Areas -- 1.3.2 1990s: Neural Networks, Fuzzy Logic, and Genetic Optimization -- 1.4 Recent Machine Learning in Digital Signal Processing -- 1.4.1 Traditional Signal Assumptions Are No Longer Valid -- 1.4.2 Encoding Prior Knowledge -- 1.4.3 Learning and Knowledge from Data -- 1.4.4 From Machine Learning to Digital Signal Processing -- 1.4.5 From Digital Signal Processing to Machine Learning -- Chapter 2 Introduction to Digital Signal Processing -- 2.1 Outline of the Signal Processing Field -- 2.1.1 Fundamentals on Signals and Systems -- 2.1.2 Digital Filtering -- 2.1.3 Spectral Analysis -- 2.1.4 Deconvolution -- 2.1.5 Interpolation -- 2.1.6 System Identification -- 2.1.7 Blind Source Separation -- 2.2 From Time-Frequency to Compressed Sensing -- 2.2.1 Time-Frequency Distributions -- 2.2.2 Wavelet Transforms -- 2.2.3 Sparsity, Compressed Sensing, and Dictionary Learning -- 2.3 Multidimensional Signals and Systems -- 2.3.1 Multidimensional Signals -- 2.3.2 Multidimensional Systems -- 2.4 Spectral Analysis on Manifolds -- 2.4.1 Theoretical Fundamentals -- 2.4.2 Laplacian Matrices -- 2.5 Tutorials and Application Examples
2.5.1 Real and Complex Signal Processing and Representations -- 2.5.2 Convolution, Fourier Transform, and Spectrum -- 2.5.3 Continuous-Time Signals and Systems -- 2.5.4 Filtering Cardiac Signals -- 2.5.5 Nonparametric Spectrum Estimation -- 2.5.6 Parametric Spectrum Estimation -- 2.5.7 Source Separation -- 2.5.8 Time-Frequency Representations and Wavelets -- 2.5.9 Examples for Spectral Analysis on Manifolds -- 2.6 Questions and Problems -- Chapter 3 Signal Processing Models -- 3.1 Introduction -- 3.2 Vector Spaces, Basis, and Signal Models -- 3.2.1 Basic Operations for Vectors -- 3.2.2 Vector Spaces -- 3.2.3 Hilbert Spaces -- 3.2.4 Signal Models -- 3.2.5 Complex Signal Models -- 3.2.6 Standard Noise Models in DSP -- 3.2.7 The Role of the Cost Function -- 3.2.8 The Role of the Regularizer -- 3.3 Digital Signal Processing Models -- 3.3.1 Sinusoidal Signal Models -- 3.3.2 System Identification Signal Models -- 3.3.3 Sinc Interpolation Models -- 3.3.4 Sparse Deconvolution -- 3.3.5 Array Processing -- 3.4 Tutorials and Application Examples -- 3.4.1 Examples of Noise Models -- 3.4.2 Autoregressive Exogenous System Identification Models -- 3.4.3 Nonlinear System Identification Using Volterra Models -- 3.4.4 Sinusoidal Signal Models -- 3.4.5 Sinc-based Interpolation -- 3.4.6 Sparse Deconvolution -- 3.4.7 Array Processing -- 3.5 Questions and Problems -- 3.A MATLAB simpleInterp Toolbox Structure -- Chapter 4 Kernel Functions and Reproducing Kernel Hilbert Spaces -- 4.1 Introduction -- 4.2 Kernel Functions and Mappings -- 4.2.1 Measuring Similarity with Kernels -- 4.2.2 Positive-Definite Kernels -- 4.2.3 Reproducing Kernel in Hilbert Space and Reproducing Property -- 4.2.4 Mercer's Theorem -- 4.3 Kernel Properties -- 4.3.1 Tikhonov's Regularization -- 4.3.2 Representer Theorem and Regularization Properties -- 4.3.3 Basic Operations with Kernels
4.4 Constructing Kernel Functions -- 4.4.1 Standard Kernels -- 4.4.2 Properties of Kernels -- 4.4.3 Engineering Signal Processing Kernels -- 4.5 Complex Reproducing Kernel in Hilbert Spaces -- 4.6 Support Vector Machine Elements for Regression and Estimation -- 4.6.1 Support Vector Regression Signal Model and Cost Function -- 4.6.2 Minimizing Functional -- 4.7 Tutorials and Application Examples -- 4.7.1 Kernel Calculations and Kernel Matrices -- 4.7.2 Basic Operations with Kernels -- 4.7.3 Constructing Kernels -- 4.7.4 Complex Kernels -- 4.7.5 Application Example for Support Vector Regression Elements -- 4.8 Concluding Remarks -- 4.9 Questions and Problems -- Part II Function Approximation and Adaptive Filtering -- Chapter 5 A Support Vector Machine Signal Estimation Framework -- 5.1 Introduction -- 5.2 A Framework for Support Vector Machine Signal Estimation -- 5.3 Primal Signal Models for Support Vector Machine Signal Processing -- 5.3.1 Nonparametric Spectrum and System Identification -- 5.3.2 Orthogonal Frequency Division Multiplexing Digital Communications -- 5.3.3 Convolutional Signal Models -- 5.3.4 Array Processing -- 5.4 Tutorials and Application Examples -- 5.4.1 Nonparametric Spectral Analysis with Primal Signal Models -- 5.4.2 System Identification with Primal Signal Model γ-filter -- 5.4.3 Parametric Spectral Density Estimation with Primal Signal Models -- 5.4.4 Temporal Reference Array Processing with Primal Signal Models -- 5.4.5 Sinc Interpolation with Primal Signal Models -- 5.4.6 Orthogonal Frequency Division Multiplexing with Primal Signal Models -- 5.5 Questions and Problems -- Chapter 6 Reproducing Kernel Hilbert Space Models for Signal Processing -- 6.1 Introduction -- 6.2 Reproducing Kernel Hilbert Space Signal Models -- 6.2.1 Kernel Autoregressive Exogenous Identification
6.2.2 Kernel Finite Impulse Response and the γ-filter -- 6.2.3 Kernel Array Processing with Spatial Reference -- 6.2.4 Kernel Semiparametric Regression -- 6.3 Tutorials and Application Examples -- 6.3.1 Nonlinear System Identification with Support VectorMachine-Autoregressive and Moving Average -- 6.3.2 Nonlinear System Identification with the γ-filter -- 6.3.3 Electric Network Modeling with Semiparametric Regression -- 6.3.4 Promotional Data -- 6.3.5 Spatial and Temporal Antenna Array Kernel Processing -- 6.4 Questions and Problems -- Chapter 7 Dual Signal Models for Signal Processing -- 7.1 Introduction -- 7.2 Dual Signal Model Elements -- 7.3 Dual Signal Model Instantiations -- 7.3.1 Dual Signal Model for Nonuniform Signal Interpolation -- 7.3.2 Dual Signal Model for Sparse Signal Deconvolution -- 7.3.3 Spectrally Adapted Mercer Kernels -- 7.4 Tutorials and Application Examples -- 7.4.1 Nonuniform Interpolation with the Dual Signal Model -- 7.4.2 Sparse Deconvolution with the Dual Signal Model -- 7.4.3 Doppler Ultrasound Processing for Fault Detection -- 7.4.4 Spectrally Adapted Mercer Kernels -- 7.4.5 Interpolation of Heart Rate Variability Signals -- 7.4.6 Denoising in Cardiac Motion-Mode Doppler Ultrasound Images -- 7.4.7 Indoor Location from Mobile Devices Measurements -- 7.4.8 Electroanatomical Maps in Cardiac Navigation Systems -- 7.5 Questions and Problems -- Chapter 8 Advances in Kernel Regression and Function Approximation -- 8.1 Introduction -- 8.2 Kernel-Based Regression Methods -- 8.2.1 Advances in Support Vector Regression -- 8.2.2 Multi-output Support Vector Regression -- 8.2.3 Kernel Ridge Regression -- 8.2.4 Kernel Signal-to-Noise Regression -- 8.2.5 Semi-supervised Support Vector Regression -- 8.2.6 Model Selection in Kernel Regression Methods -- 8.3 Bayesian Nonparametric Kernel Regression Models
8.3.1 Gaussian Process Regression -- 8.3.2 Relevance Vector Machines -- 8.4 Tutorials and Application Examples -- 8.4.1 Comparing Support Vector Regression, Relevance Vector Machines, and Gaussian Process Regression -- 8.4.2 Profile-Dependent Support Vector Regression -- 8.4.3 Multi-output Support Vector Regression -- 8.4.4 Kernel Signal-to-Noise Ratio Regression -- 8.4.5 Semi-supervised Support Vector Regression -- 8.4.6 Bayesian Nonparametric Model -- 8.4.7 Gaussian Process Regression -- 8.4.8 Relevance Vector Machines -- 8.5 Concluding Remarks -- 8.6 Questions and Problems -- Chapter 9 Adaptive Kernel Learning for Signal Processing -- 9.1 Introduction -- 9.2 Linear Adaptive Filtering -- 9.2.1 Least Mean Squares Algorithm -- 9.2.2 Recursive Least-Squares Algorithm -- 9.3 Kernel Adaptive Filtering -- 9.4 Kernel Least Mean Squares -- 9.4.1 Derivation of Kernel Least Mean Squares -- 9.4.2 Implementation Challenges and Dual Formulation -- 9.4.3 Example on Prediction of the Mackey-Glass Time Series -- 9.4.4 Practical Kernel Least Mean Squares Algorithms -- 9.5 Kernel Recursive Least Squares -- 9.5.1 Kernel Ridge Regression -- 9.5.2 Derivation of Kernel Recursive Least Squares -- 9.5.3 Prediction of the Mackey-Glass Time Series with Kernel Recursive Least Squares -- 9.5.4 Beyond the Stationary Model -- 9.5.5 Example on Nonlinear Channel Identification and Reconvergence -- 9.6 Explicit Recursivity for Adaptive Kernel Models -- 9.6.1 Recursivity in Hilbert Spaces -- 9.6.2 Recursive Filters in Reproducing Kernel Hilbert Spaces -- 9.7 Online Sparsification with Kernels -- 9.7.1 Sparsity by Construction -- 9.7.2 Sparsity by Pruning -- 9.8 Probabilistic Approaches to Kernel Adaptive Filtering -- 9.8.1 Gaussian Processes and Kernel Ridge Regression -- 9.8.2 Online Recursive Solution for Gaussian Processes Regression
9.8.3 Kernel Recursive Least Squares Tracker
Description based on publisher supplied metadata and other sources
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2020. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries
鏈接 Print version: Rojo-Alvarez, Jose Luis Digital Signal Processing with Kernel Methods Somerset : John Wiley & Sons, Incorporated,c2018 9781118611791
主題 Signal processing-Digital techniques
Electronic books
Alt Author Martinez-Ramon, Manel
Munoz-Mari, Jordi
Camps-Valls, Gustau
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