Academic Press Library in Signal Processing, Vol 1

  • ID: 2485501
  • Book
  • 1480 Pages
  • Elsevier Science and Technology
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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
  • 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
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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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