Kernel-based Data Fusion for Machine Learning

Methods and Applications in Bioinformatics and Text Mining

  • Shi Yu
  • Léon-Charles Tranchevent
  • Bart De Moor
  • Yves Moreau

Part of the Studies in Computational Intelligence book series (SCI, volume 345)

Table of contents

  1. Front Matter
  2. Shi Yu, Léon-Charles Tranchevent, Bart De Moor, Yves Moreau
    Pages 1-26
  3. Shi Yu, Léon-Charles Tranchevent, Bart De Moor, Yves Moreau
    Pages 27-37
  4. Shi Yu, Léon-Charles Tranchevent, Bart De Moor, Yves Moreau
    Pages 39-88
  5. Shi Yu, Léon-Charles Tranchevent, Bart De Moor, Yves Moreau
    Pages 89-107
  6. Shi Yu, Léon-Charles Tranchevent, Bart De Moor, Yves Moreau
    Pages 109-144
  7. Shi Yu, Léon-Charles Tranchevent, Bart De Moor, Yves Moreau
    Pages 145-172
  8. Shi Yu, Léon-Charles Tranchevent, Bart De Moor, Yves Moreau
    Pages 173-190
  9. Shi Yu, Léon-Charles Tranchevent, Bart De Moor, Yves Moreau
    Pages 191-205
  10. Shi Yu, Léon-Charles Tranchevent, Bart De Moor, Yves Moreau
    Pages 207-208
  11. Back Matter

About this book

Introduction

Data fusion problems arise frequently in many different fields.  This book provides a specific introduction to data fusion problems using support vector machines. In the first part, this book begins with a brief survey of additive models and Rayleigh quotient objectives in machine learning, and then introduces kernel fusion as the additive expansion of support vector machines in the dual problem.  The second part presents several novel kernel fusion algorithms and some real applications in supervised and unsupervised learning. The last part of the book substantiates the value of the proposed theories and algorithms in MerKator, an open software to identify disease relevant genes based on the integration of heterogeneous genomic data sources in multiple species.


The topics presented in this book are meant for researchers or students who use support vector machines. Several topics addressed in the book may also be interesting to computational biologists who want to tackle data fusion challenges in real applications. The background required of the reader is a good knowledge of data mining, machine learning and linear algebra.

 

Keywords

Bioinformatics Computational Intelligence Data Fusion Kernel Method Text Mining

Authors and affiliations

  • Shi Yu
    • 1
  • Léon-Charles Tranchevent
    • 2
  • Bart De Moor
    • 3
  • Yves Moreau
    • 2
  1. 1.Department of Medicine, Institute for Genomics and Systems Biology Knapp Center for Biomedical Discovery University of ChicagoChicagoUSA
  2. 2.Department of Electrical Engineering, Bioinformatics Group, SCD-SISTA Katholieke Universiteit LeuvenHeverlee-LeuvenBelgium
  3. 3.Department of Electrical Engineering SCD-SISTAKatholieke Universiteit LeuvenHeverlee-LeuvenBelgium

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-19406-1
  • Copyright Information Springer Berlin Heidelberg 2011
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-19405-4
  • Online ISBN 978-3-642-19406-1
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • About this book
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