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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...