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Academic Press Library in Signal Processing
 
 

Academic Press Library in Signal Processing, 1st Edition

Signal Processing Theory and Machine Learning

 
Academic Press Library in Signal Processing, 1st Edition,Sergios Theodoridis,Rama Chellappa,ISBN9780123965028
 
 
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Theodoridis  &   Chellappa   

Academic Press

9780123965028

9780123972262

1480

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Quickly grasp and understand the key principles in Theory, Speech and Acoustic Processing, and Machine Learning from world leading experts

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Key Features

  • Quick tutorial reviews of important and emerging topics of research in machine learning
  • Presents core principles in signal processing theory and shows their applications
  • Reference content on core principles, technologies, algorithms and applications
  • Comprehensive references to journal articles and other literature on which to build further, more specific and detailed knowledge
  • Edited by leading people in the field who, through their reputation, have been able to commission experts to write on a particular topic

Description

This first volume, edited and authored by world leading experts, gives a review of the principles, methods and techniques of important and emerging research topics and technologies in machine learning and advanced signal processing theory.

With this reference source you will:

  • Quickly grasp a new area of research 
  • Understand the underlying principles of a topic and its application
  • Ascertain how a topic relates to other areas and learn of the research issues yet to be resolved

Readership

PhD students

Post Docs

R&D engineers in signal processing and wireless and mobile communications

Consultants

Sergios Theodoridis

Sergios Theodoridis acquired a Physics degree with honors from the University of Athens, Greece in 1973 and a MSc and a Ph.D. degree in Signal Processing and Communications from the University of Birmingham, UK in 1975 and 1978 respectively. Since 1995 he has been a Professor with the Department of Informatics and Communications at the University of Athens.

Affiliations and Expertise

Department of Informatics and Telecommunications, University of Athens, Greece

View additional works by Sergios Theodoridis

Rama Chellappa

Affiliations and Expertise

University of Maryland, College Park, MD, USA

View additional works by Rama Chellappa

Academic Press Library in Signal Processing, 1st Edition

Introduction

Signal Processing at Your Fingertips!

About the Editors

Section Editors

Section 1

Section 2

Authors Biography

Chapter 1

Chapter 2

Chapter 3

Chapter 4

Chapter 5

Chapter 6

Chapter 7

Chapter 8

Chapter 9

Chapter 10

Chapter 11

Chapter 12

Chapter 14

Chapter 15

Chapter 16

Chapter 17

Chapter 18

Chapter 19

Chapter 20

Chapter 21

Chapter 22

Chapter 24

Chapter 25

Chapter 26

Section 1: SIGNAL PROCESSING THEORY

Chapter 1. Introduction to Signal Processing Theory

Abstract

1.01.1 Introduction

1.01.2 Continuous-time signals and systems

1.01.3 Discrete-time signals and systems

1.01.4 Random signals and stochastic processes

1.01.5 Sampling and quantization

1.01.6 FIR and IIR filter design

1.01.7 Digital filter structures and implementations

1.01.8 Multirate signal processing

1.01.9 Filter banks and wavelets

1.01.10 Discrete multiscale and transforms

1.01.11 Frames

1.01.12 Parameter estimation

1.01.13 Adaptive filtering

1.01.14 Closing comments

References

Chapter 2. Continuous-Time Signals and Systems

Abstract

Nomenclature

1.02.1 Introduction

1.02.2 Continuous-time systems

1.02.3 Differential equations

1.02.4 Laplace transform: definition and properties

1.02.5 Transfer function and stability

1.02.6 Frequency response

1.02.7 The Fourier series and the Fourier transform

1.02.8 Conclusion and future trends

1.02.9 Relevant Websites:

1.02.10 Supplementary data

1.02.11 Supplementary data

Glossary

References

Chapter 3. Discrete-Time Signals and Systems

Abstract

1.03.1 Introduction

1.03.2 Discrete-time signals: sequences

1.03.3 Discrete-time systems

1.03.4 Linear time-invariant (LTI) systems

1.03.5 Discrete-time signals and systems with MATLAB

1.03.6 Conclusion

References

Chapter 4. Random Signals and Stochastic Processes

Abstract

Acknowledgements

1.04.1 Introduction

1.04.2 Probability

1.04.3 Random variable

1.04.4 Random process

References

Chapter 5. Sampling and Quantization

Abstract

1.05.1 Introduction

1.05.2 Preliminaries

1.05.3 Sampling of deterministic signals

1.05.4 Sampling of stochastic processes

1.05.5 Nonuniform sampling and generalizations

1.05.6 Quantization

1.05.7 Oversampling techniques

1.05.8 Discrete-time modeling of mixed-signal systems

References

Chapter 6. Digital Filter Structures and Their Implementation

Abstract

1.06.1 Introduction

1.06.2 Digital FIR filters

1.06.3 The analog approximation problem

1.06.4 Doubly resistively terminated lossless networks

1.06.5 Ladder structures

1.06.6 Lattice structures

1.06.7 Wave digital filters

1.06.8 Frequency response masking (FRM) structure

1.06.9 Computational properties of filter algorithms

1.06.10 Architecture

1.06.11 Arithmetic operations

1.06.12 Sum-of-products (SOP)

1.06.13 Power reduction techniques

References

Chapter 7. Multirate Signal Processing for Software Radio Architectures

Abstract

1.07.1 Introduction

1.07.2 The Sampling process and the “Resampling” process

1.07.3 Digital filters

1.07.4 Windowing

1.07.5 Basics on multirate filters

1.07.6 From single channel down converter to standard down converter channelizer

1.07.7 Modifications of the standard down converter channelizer—M:2 down converter channelizer

1.07.8 Preliminaries on software defined radios

1.07.9 Proposed architectures for software radios

1.07.10 Closing comments

Glossary

References

Chapter 8. Modern Transform Design for Practical Audio/Image/Video Coding Applications

Abstract

1.8.1 Introduction

1.8.2 Background and fundamentals

1.8.3 Design strategy

1.8.4 Approximation approach via direct scaling

1.8.5 Approximation approach via structural design

1.8.6 Wavelet filters design via spectral factorization

1.8.7 Higher-order design approach via optimization

1.8.8 Conclusion

References

Chapter 9. Discrete Multi-Scale Transforms in Signal Processing

Abstract

1.09.1 Introduction

1.09.2 Wavelets: a multiscale analysis tool

1.09.3 Curvelets and their applications

1.09.4 Contourlets and their applications

1.09.5 Shearlets and their applications

A Appendix

References

Chapter 10. Frames in Signal Processing

Abstract

1.10.1 Introduction

1.10.2 Basic concepts

1.10.3 Relevant definitions

1.10.4 Some computational remarks

1.10.5 Construction of frames from a prototype signal

1.10.6 Some remarks and highlights on applications

1.10.7 Conclusion

References

Chapter 11. Parametric Estimation

Abstract

1.11.1 Introduction

1.11.2 Deterministic and stochastic signals

1.11.3 Parametric models for signals and systems

References

Chapter 12. Adaptive Filters

Abstract

Acknowledgment

1.12.1 Introduction

1.12.2 Optimum filtering

1.12.3 Stochastic algorithms

1.12.4 Statistical analysis

1.12.5 Extensions and current research

1.12.6 Supplementary data

References

Section 2: MACHINE LEARNING

Chapter 13. Introduction to Machine Learning

Abstract

Acknowledgments

1.13.1 Scope and context

1.13.2 Contributions

References

Chapter 14. Learning Theory

Abstract

1.14.1 Introduction

1.14.2 Probabilistic formulation of learning problems

1.14.3 Uniform convergence of empirical means

1.14.4 Model selection

1.14.5 Alternatives to uniform convergence

1.14.6 Computational aspects

1.14.7 Beyond the basic probabilistic framework

1.14.8 Conclusions and future trends

Glossary

Relevant websites

References

Chapter 15. Neural Networks

Abstract

1.15.1 Introduction

1.15.2 Learning with single neurons

1.15.3 Recurrent neural networks

1.15.4 Learning by focussing on the generalization ability

1.15.5 Unsupervised learning

1.15.6 Applications

1.15.7 Open issues and problems

1.15.8 Implementation, code, and data sets

1.15.9 Conclusions and future trends

Glossary

References

Chapter 16. Kernel Methods and Support Vector Machines

Abstract

Nomenclature

Acknowledgment

1.16.1 Introduction

1.16.2 Foundations of kernel methods

1.16.3 Fundamental kernel methods

1.16.4 Computational issues of kernel methods

1.16.5 Multiple kernel learning

1.16.6 Applications

1.16.7 Open issues and problems

Glossary

References

Chapter 17. Online Learning in Reproducing Kernel Hilbert Spaces

Abstract

Nomenclature

1.17.1 Introduction

1.17.2 Parameter estimation: The regression and classification tasks

1.17.3 Overfitting and regularization

1.17.4 Mapping a nonlinear to a linear task

1.17.5 Reproducing Kernel Hilbert spaces

1.17.6 Least squares learning algorithms

1.17.7 A convex analytic toolbox for online learning

1.17.8 Related work and applications

1.17.9 Conclusions

Appendices

B Proof of Proposition 60

C Proof of convergence for Algorithm 61

References

Chapter 18. Introduction to Probabilistic Graphical Models

Abstract

Nomenclature

Acknowledgments

1.18.1 Introduction

1.18.2 Preliminaries

1.18.3 Representations

1.18.4 Learning

1.18.5 Inference

1.18.6 Applications

1.18.7 Implementation/code

1.18.8 Data sets

1.18.9 Conclusion

Glossary

References

Chapter 19. A Tutorial Introduction to Monte Carlo Methods, Markov Chain Monte Carlo and Particle Filtering

Abstract

1.19.1 Introduction

1.19.2 The Monte Carlo principle

1.19.3 Basic techniques for simulating random variables

1.19.4 Markov Chain Monte Carlo

1.19.5 Sequential Monte Carlo

1.19.6 Advanced Monte Carlo methods

1.19.7 Open issues and problems

1.19.8 Further reading

Glossary

References

Chapter 20. Clustering

Abstract

1.20.1 Introduction

1.20.2 Clustering algorithms

1.20.3 Clustering validation

1.20.4 Applications

1.20.5 Open issues and problems

1.20.6 Conclusion

Glossary

References

Chapter 21. Unsupervised Learning Algorithms and Latent Variable Models: PCA/SVD, CCA/PLS, ICA, NMF, etc.

Abstract

1.21.1 Introduction and of problems statement

1.21.2 PCA/SVD and related problems

1.21.3 ICA and related problems

1.21.4 NMF and related problems

1.21.5 Future directions: constrained multi-block tensor factorizations and multilinear blind source separation

1.21.6 Summary

References

Chapter 22. Semi-Supervised Learning

Abstract

1.22.1 Introduction

1.22.2 Semi-supervised learning algorithms

1.22.3 Semi-supervised learning for structured outputs

1.22.4 Large scale semi-supervised learning

1.22.5 Theoretical analysis overview

1.22.6 Challenges

Glossary

References

Relevant websites

Chapter 23. Sparsity-Aware Learning and Compressed Sensing: An Overview

1.23.1 Introduction

1.23.2 Parameter estimation

1.23.3 Searching for a norm

1.23.4 The least absolute shrinkage and selection operator (LASSO)

1.23.5 Sparse signal representation

1.23.6 In quest for the sparsest solution

1.23.7 Uniqueness of the minimizer

1.23.8 Equivalence of and minimizers: sufficiency conditions

1.23.9 Robust sparse signal recovery from noisy measurements

1.23.10 Compressed sensing: the glory of randomness

1.23.11 Sparsity-promoting algorithms

1.23.12 Variations on the sparsity-aware theme

1.23.13 Online time-adaptive sparsity-promoting algorithms

1.23.14 Learning sparse analysis models

1.23.15 A case study: time-frequency analysis

1.23.16 From sparse vectors to low rank matrices: a highlight

1.23.17 Conclusions

Appendix

References

Chapter 24. Information Based Learning

1.24.1 Introduction

1.24.2 Information theoretic descriptors

1.24.3 Unifying information theoretic framework for machine learning

1.24.4 Nonparametric information estimators

1.24.5 Reproducing kernel Hilbert space framework for ITL

1.24.6 Information particle interaction for learning from samples

1.24.7 Illustrative examples

1.24.8 Conclusions and future trends

References

Chapter 25. A Tutorial on Model Selection

Abstract

1.25.1 Introduction

1.25.2 Minimum distance estimation criteria

1.25.3 Bayesian approaches to model selection

1.25.4 Model selection by compression

1.25.5 Simulation

References

Chapter 26. Music Mining

Abstract

Acknowledgments

1.26.1 Introduction

1.26.2 Ground truth acquisition and evaluation

1.26.3 Audio feature extraction

1.26.4 Extracting context information about music

1.26.5 Similarity search

1.26.6 Classification

1.26.7 Tag annotation

1.26.8 Visualization

1.26.9 Advanced music mining

1.26.10 Software and datasets

1.26.11 Open problems and future trends

1.26.12 Further reading

Glossary

References

Index

 
 
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