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
Inhalt
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