Buch, Englisch, 672 Seiten, Format (B × H): 175 mm x 250 mm, Gewicht: 1312 g
Reihe: Wiley - IEEE
Buch, Englisch, 672 Seiten, Format (B × H): 175 mm x 250 mm, Gewicht: 1312 g
Reihe: Wiley - IEEE
ISBN: 978-1-118-61179-1
Verlag: Wiley
A realistic and comprehensive review of joint approaches to machine learning and signal processing algorithms, with application to communications, multimedia, and biomedical engineering systems
Digital Signal Processing with Kernel Methods reviews the milestones in the mixing of classical digital signal processing models and advanced kernel machines statistical learning tools. It explains the fundamental concepts from both fields of machine learning and signal processing so that readers can quickly get up to speed in order to begin developing the concepts and application software in their own research.
Digital Signal Processing with Kernel Methods provides a comprehensive overview of kernel methods in signal processing, without restriction to any application field. It also offers example applications and detailed benchmarking experiments with real and synthetic datasets throughout. Readers can find further worked examples with Matlab source code on a website developed by the authors: http://github.com/DSPKM
• Presents the necessary basic ideas from both digital signal processing and machine learning concepts
• Reviews the state-of-the-art in SVM algorithms for classification and detection problems in the context of signal processing
• Surveys advances in kernel signal processing beyond SVM algorithms to present other highly relevant kernel methods for digital signal processing
An excellent book for signal processing researchers and practitioners, Digital Signal Processing with Kernel Methods will also appeal to those involved in machine learning and pattern recognition.
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Technische Informatik Hardware: Grundlagen und Allgemeines
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
- Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Signalverarbeitung
Weitere Infos & Material
About the Authors xiii
Preface xvii
Acknowledgements xxi
List of Abbreviations xxiii
Part I Fundamentals and Basic Elements 1
1 From Signal Processing to Machine Learning 3
1.1 A New Science is Born: Signal Processing 3
1.1.1 Signal Processing Before Being Coined 3
1.1.2 1948: Birth of the Information Age 4
1.1.3 1950s: Audio Engineering Catalyzes Signal Processing 4
1.2 From Analog to Digital Signal Processing 5
1.2.1 1960s: Digital Signal Processing Begins 5
1.2.2 1970s: Digital Signal Processing Becomes Popular 6
1.2.3 1980s: Silicon Meets Digital Signal Processing 6
1.3 Digital Signal Processing Meets Machine Learning 7
1.3.1 1990s: New Application Areas 7
1.3.2 1990s: Neural Networks, Fuzzy Logic, and Genetic Optimization 7
1.4 Recent Machine Learning in Digital Signal Processing 8
1.4.1 Traditional Signal Assumptions Are No Longer Valid 8
1.4.2 Encoding Prior Knowledge 8
1.4.3 Learning and Knowledge from Data 9
1.4.4 From Machine Learning to Digital Signal Processing 9
1.4.5 From Digital Signal Processing to Machine Learning 10
2 Introduction to Digital Signal Processing 13
2.1 Outline of the Signal Processing Field 13
2.1.1 Fundamentals on Signals and Systems 14
2.1.2 Digital Filtering 21
2.1.3 Spectral Analysis 24
2.1.4 Deconvolution 28
2.1.5 Interpolation 30
2.1.6 System Identification 31
2.1.7 Blind Source Separation 36
2.2.3 Sparsity, Compressed Sensing, and Dictionary Learning 44
2.3 Multidimensional Signals and Systems 48
2.3.1 Multidimensional Signals 49
2.3.2 Multidimensional Systems 51
2.4 Spectral Analysis on Manifolds 52
2.4.1 Theoretical Fundamentals 52
2.4.2 Laplacian Matrices 54
2.5 Tutorials and Application Examples 57
2.5.1 Real and Complex Signal Processing and Representations 57
2.
