Academic Press Library in Signal Processing book cover

Academic Press Library in Signal Processing

Signal Processing Theory and Machine Learning

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

Audience

PhD students

Post Docs

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

Consultants

,

Published: September 2013

Imprint: Academic Press

ISBN: 978-0-12-397226-2

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

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