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
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science & Techn.
Dateigröße
ISBN-13
978-0-12-397226-2 (9780123972262)
Schweitzer Klassifikation
1. Introduction to Signal Processing Theory 2. Continuous-Time Signals and Systems 3. Discrete-Time Signals and Systems 4. Random Signals and Stochastic Processes 5. Sampling and Quantization 6. Digital Filter Structures and their Implementation 7. Multirate Signal Processing for Software Radio Architectures 8. Modern Transform Design for Practical Audio/Image/Video Coding Applications 9. Discrete Multi-Scale Transforms in Signal Processing 10. Frames in Signal Processing 11. Parametric Estimation 12. Adaptive Filters 13. Introduction to Machine Learning 14. Learning Theory 15. Neural Networks 16. Kernel Methods and Support Vector Machines 17. Online Learning in Reproducing Kernel Hilbert Spaces 18. Introduction to Probabilistic Graphical Models 19. A Tutorial Introduction to Monte Carlo Methods, Markov Chain Monte Carlo and Particle Filtering 20. Clustering 21. Unsupervised Learning Algorithms and Latent Variable Models: PCA/SVD, CCA/PLS, ICA, NMF, etc. 22. Semi-Supervised Learning 23. Sparsity-Aware Learning and Compressed Sensing: An Overview 24. Information Based Learning 25. A Tutorial on Model Selection 26. Music Mining
