Über den Autor
Gustavo Camps-Valls 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 Valencia, Valencia, in 1996, 1998, and 2002, respectively. He is currently an Assistant Professor in the Department of Electronics Engineering, Universitat de Valencia, 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. Dr. Manel Martinez-Ramon is an associate professor at the Dpt. Signal Theory and Communications, at Universidad Carlos III de Madrid. He is currently doing research with the Group of Information Management and Processing and teaching several undergraduate and graduate courses in the area of knowledge of signal processing and communications. He has worked in several institutions including the University of New Mexico, USA and the Universidad Politecnica de Cartagena, Spain. He has participated in many research projects and published 40 papers in international journals and conferences about neural networks, machine learning and its applications to signal processing. Dr. Jose Luis Rojo Alvarez is an associate professor at the Department of Signal Theory and Communications in University Carlos III of Madrid (Spain), where he teaches Systems and Circuits and related topics. He received a B.Sc. (1996) and the Ph.D. (2000) degrees on Telecommunication Engineering at University of Vigo and University Politecnica de Madrid, respectively. His research interests focus on statistical learning methods for signal and image processing, arrhythmia mechanisms, robust signal processing methods for cardiac repolarization, and Doppler image post-processing. He has (co)authored 25 international papers and has contributed to more than 60 conference proceedings.
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.