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Diniz / Naylor / Suykens

Academic Press Library in Signal Processing, Volume 1: Signal Processing Theory and Machine Learning

Medium: Buch
ISBN: 978-0-12-396502-8
Verlag: PAPERBACKSHOP UK IMPORT
Erscheinungstermin: 05.09.2013
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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

Produkteigenschaften

  • Artikelnummer: 9780123965028
  • Medium: Buch
  • ISBN: 978-0-12-396502-8
  • Verlag: PAPERBACKSHOP UK IMPORT
  • Erscheinungstermin: 05.09.2013
  • Sprache(n): Englisch
  • Auflage: Neuausgabe 2013
  • Serie: Academic Press Library in Sign
  • Produktform: Gebunden
  • Gewicht: 2664 g
  • Seiten: 1480
  • Format (B x H x T): 197 x 241 x 63 mm
  • Ausgabetyp: Kein, Unbekannt

Themen

Autoren/Hrsg.

Herausgeber

Paulo S. R. Diniz's teaching and research interests are in analog and digital signal processing, adaptive signal processing, digital communications, wireless communications, multirate systems, stochastic processes, and electronic circuits. He has published over 300 refereed papers in some of these areas and wrote two textbooks and a research book. He has received awards for best papers and technical achievements

Weitere Mitwirkende

Sergios Theodoridis acquired a Physics degree with honors from the University of Athens, Greece in 1973 and a MSc and a Ph.D. degree in Signal Processing and Communications from the University of Birmingham, UK in 1975 and 1978 respectively. Since 1995 he has been a Professor with the Department of Informatics and Communications at the University of Athens.

Prof. Rama Chellappa received the B.E. (Hons.) degree from the University of Madras, India, in 1975 and the M.E. (Distinction) degree from Indian Institute of Science, Bangalore, in 1977. He received M.S.E.E. and Ph.D. Degrees in Electrical Engineering from Purdue University, West Lafayette, IN, in 1978 and 1981 respectively. Since 1991, he has been a Professor of Electrical Engineering and an affiliate Professor of Computer Science at University of Maryland, College Park. He is also affiliated with the Center for Automation Research (Director) and the Institute for Advanced Computer Studies (Permanent Member). In 2005, he was named a Minta Martin Professor of Engineering. Prior to joining the University of Maryland, he was an Assistant (1981-1986) and Associate Professor (1986-1991) and Director of the Signal and Image Processing Institute (1988-1990) at University of Southern California, Los Angeles. Over the last 29 years, he has published numerous book chapters, peer-reviewed journal and conference papers. He has co-authored and edited books on MRFs, face and gait recognition and collected works on image processing and analysis. His current research interests are face and gait analysis, markerless motion capture, 3D modeling from video, image and video-based recognition and exploitation and hyper spectral processing.

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