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Kernel Methods in Bioengineering, Signal and Image Processing

By: Gustavo Camps-Valls (Editor), Jose Luis Rojo-Alvarez (Editor), Manel Martinez-Ramon (Editor)

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Hardcover | 30 November 2006

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Hardcover
432 Pages

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25.4 x 17.78 x 2.39

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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 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.

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Kernel methods : a paradigm for pattern analysis	p. 1
Kernel methods in genomics and computational biology	p. 42
Kernel clustering for knowledge discovery in clinical microarray data analysis	p. 64
Support vector machine for recognition of white blood cells of leukaemia	p. 93
Classification of multiple interleaved human brain tasks in functional magnetic resonance imaging	p. 123
Discrete time signal processing framework with support vector machines	p. 150
A complex support vector machine approach to OFDM coherent demodulation	p. 179
Comparison of kernel methods applied to smart antenna array processing	p. 203
Applications of kernel theory to speech recognition	p. 224
Building sequence kernels for speaker verification and word recognition	p. 246
A kernel canonical correlation analysis for learning the semantics of text	p. 263
On the pre-image problem in kernel methods	p. 284
Perceptual image representations for support vector machine image coding	p. 303
Image classification and retrieval with kernel methods	p. 325
Probabilistic kernel PCA and its application to statistical modeling and inference	p. 346
Hyperspectral image classification with kernels	p. 374
Table of Contents provided by Blackwell. All Rights Reserved.

Kernel Methods in Bioengineering, Signal and Image Processing

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Hardcover

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