Academic Press Library in Signal Processing: Volume 1

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 applicationAscertain 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 learningPresents core principles in signal processing theory and shows their applicationsReference 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 INDICE: 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

• ISBN: 978-0-12-396502-8
• Editorial: Academic Press
• Encuadernacion: Cartoné
• Páginas: 1480
• Fecha Publicación: 05/09/2013
• Nº Volúmenes: 1
• Idioma: Inglés