In the last decade, a number of powerful kernel-based learning methods have been proposed in the machine learning community: support vector machines (SVMs), kernel fisher discriminant (KFD) analysis, kernel PCA/ICA, kernel mutual information, kernel k-means, and kernel ARMA. Successful applications of these algorithms have been reported in many fields, such as medicine, bioengineering, communications, audio and image processing, and computational biology and bioinformatics. Kernel Methods in Bioengineering, Signal and Image Processing covers real-world applications, such as computational biology, text categorization, time series prediction, interpolation, system identification, speech recognition, image de-noising, image coding, classification, and segmentation. Kernel Methods in Bioengineering, Signal and Image Processing encompasses the vast field of kernel methods from a multidisciplinary approach by presenting chapters dedicated to adaptation and use of kernel methods in the selected areas of bioengineering, signal processing and communications, and image processing.

About the Author

Gustavo Camps-Valls was born in Val�ncia, Spain, in 1972. He received the B.Sc. degree in physics, the B.Sc. degree in electronics engineering, and the Ph.D. degree in physics from the Universitat de Val�ncia, Val�ncia, in 1996, 1998, and 2002, respectively. He is currently an Assistant Professor in the Department of Electronics Engineering, Universitat de Val�ncia, where he teaches electronics, advanced time-series processing, and digital signal processing. His research interests are neural networks and kernel methods for hyperspectral data analysis, health sciences, and safety-related areas. He has contributed to 30 journal papers, several book chapters, and more than 75 international conference papers. Camps Valls referees several international journals and has served on the Scientific Committees of several national and international conferences. He is a member of the European Network on Intelligent Technologies for Smart Adaptive Systems and is actively involved in the Spanish Thematic Network for Pattern Recognition.

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