Academic Press Library in Signal Processing

Signal Processing Theory and Machine Learning


  • Sergios Theodoridis, Department of Informatics and Telecommunications, University of Athens, Greece
  • Rama Chellappa, University of Maryland, College Park, MD, USA

Section Editor:

  • Paulo Diniz
  • Patrick Naylor
  • Johan Suykens

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

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



Book information

  • Published: September 2013
  • ISBN: 978-0-12-397226-2

Table of Contents

CHAPTER 1 Introduction to Signal Processing Theory- Isabela Ferrão Apolinário and Paulo S.R. Diniz

CHAPTER 2 Continuous-Time Signals and Systems- José Antonio Apolinário Jr. and Carla L. Pagliari

CHAPTER 3 Discrete-Time Signals and Systems- Leonardo Gomes Baltar and Josef A. Nossek

CHAPTER 4 Random Signals and Stochastic Processes- Luiz Wagner Pereira Biscainho

CHAPTER 5 Sampling and Quantization- HÃ¥kan Johansson

CHAPTER 6 Digital Filter Structures and their Implementation- Lars Wanhammar and Ya Jun Yu

CHAPTER 7 Multirate Signal Processing for Software Radio Architectures- Fred Harris, Elettra Venosa and Xiaofei Chen

CHAPTER 8 Modern Transform Design for Practical Audio/Image/Video Coding Applications- Trac D. Tran

CHAPTER 9 Discrete Multi-Scale Transforms in Signal Processing- Yufang Bao and Hamid Krim

CHAPTER 10 Frames in Signal Processing- Lisandro Lovisolo and Eduardo A. B. da Silva

CHAPTER 11 Parametric Estimation- Suleyman Serdar Kozat and Andrew C. Singer

CHAPTER 12 Adaptive Filters- Vítor H. Nascimento and Magno T. M. Silva

CHAPTER 13 Introduction to Machine Learning- Johan A.K. Suykens

CHAPTER 14 Learning Theory- Ambuj Tewari and Peter L. Bartlett

CHAPTER 15 Neural Networks- Barbara Hammer

CHAPTER 16 Kernel Methods and Support Vector Machines- John Shawe-Taylor and Shiliang Sun

CHAPTER 17 Online Learning in Reproducing Kernel Hilbert Spaces- Konstantinos Slavakis, Pantelis Bouboulis and Sergios Theodoridis

CHAPTER 18 Introduction to Probabilistic Graphical Models- Franz Pernkopf, Robert Peharz and Sebastian Tschiatschek

CHAPTER 19 A Tutorial Introduction to Monte Carlo Methods, Markov Chain Monte Carlo and Particle Filtering- A. Taylan Cemgil

CHAPTER 20 Clustering- Dao Lam and Donald C.Wunsch

CHAPTER 21 Unsupervised Learning Algorithms and Latent Variable Models: PCA/SVD, CCA/PLS, ICA, NMF, etc- Andrzej Cichocki

CHAPTER 22 Semi-Supervised Learning- Xueyuan Zhou and Mikhail Belkin

CHAPTER 23 Sparsity-Aware Learning and Compressed Sensing: An Overview- Sergios Theodoridis, Yannis Kopsinis and Konstantinos Slavakis

CHAPTER 24 Information Based Learning- José C. Principe, Badong Chen and Luis Gonzalo Sánchez Giraldo

CHAPTER 25 A Tutorial on Model Selection- Enes Makalic, Daniel Francis Schmidt and Abd-Krim Seghouane

CHAPTER 26 Music Mining- George Tzanetakis