Academic Press Library in Signal Processing: Volume 1 book cover

Academic Press Library in Signal Processing: Volume 1

Signal Processing Theory and Machine Learning

This first volume of a four volume set, 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: 978-0-12-396502-8

Contents

  • CHAPTER 1 Introduction to Signal Processing Theory

    CHAPTER 2 Continuous-Time Signals and Systems

    CHAPTER 3 Discrete-Time Signals and Systems

    CHAPTER 4 Random Signals and Stochastic Processes

    CHAPTER 5 Sampling and Quantization

    CHAPTER 6 Digital Filter Structures and their Implementation

    CHAPTER 7 Multirate Signal Processing for Software Radio Architectures

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

    CHAPTER 9 Discrete Multi-Scale Transforms in Signal Processing

    CHAPTER 10 Frames in Signal Processing

    CHAPTER 11 Parametric Estimation

    CHAPTER 12 Adaptive Filters

    CHAPTER 13 Introduction to Machine Learning

    CHAPTER 14 Learning Theory

    CHAPTER 15 Neural Networks

    CHAPTER 16 Kernel Methods and Support Vector Machines

    CHAPTER 17 Online Learning in Reproducing Kernel Hilbert Spaces

    CHAPTER 18 Introduction to Probabilistic Graphical Models

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

    CHAPTER 20 Clustering

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

    CHAPTER 22 Semi-Supervised Learning

    CHAPTER 23 Sparsity-Aware Learning and Compressed Sensing: An Overview

    CHAPTER 24 Information Based Learning

    CHAPTER 25 A Tutorial on Model Selection

    CHAPTER 26 Music Mining

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