Kernel Methods in Bioengineering, Signal and Image Processing, 1st Edition

  • Published By:
  • ISBN-10: 1599040441
  • ISBN-13: 9781599040448
  • Grade Level Range: College Freshman - College Senior
  • 300 Pages | eBook
  • Original Copyright 2007 | Published/Released February 2007
  • This publication's content originally published in print form: 2007

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About

Overview

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.

Table of Contents

Front Cover.
Title Page.
Copyright Page.
Kernel Methods in Bioengineering, Signal and Image Processing: Table of Contents.
Foreword.
Preface.
Acknowledgments.
1: Kernel Methods: A Paradigm for Pattern Analysis.
2: Bio-Medical Engineering.
3: Kernel Methods in Genomics and Computational Biology.
4: Kernel Clustering for Knowledge Discovery in Clinical Microarray Data Analysis.
5: Support Vector Machine for Recognition of White Blood Cells of Leukaemia.
6: Classification of Multiple Interleaved Human Brain Tasks in Functional Magnetic Resonance Imaging.
7: Signal Processing.
8: Discrete Time Signal Processing Framework with Support Vector Machines.
9: A Complex Support Vector Machine Approach to OFDM Coherent Demodulation.
10: Comparison of Kernel Methods Applied to Smart Antenna Array Processing.
11: Applications of Kernel Theory to Speech Recognition.
12: Building Sequence Kernels for Speaker Verification and Word Recognition.
13: A Kernel Canonical Correlation Analysis for Learning the Semantics of Text.
14: Image Processing.
15: On the Pre-Image Problem in Kernel Methods.
16: Perceptual Image Representations for Support Vector Machine Image Coding.
17: Image Classification and Retrieval with Kernel Methods.
18: Probabilistic Kernel PCA and its Application to Statistical Modeling and Inference.
19: Hyperspectral Image Classification with Kernels.
About the Authors.
Index.