Overview
- Recent research on Kernel-based Data Fusion for Machine Learning
- Presents methods and applications in bioinformatics and text mining
- Written by leading experts in the field
Part of the book series: Studies in Computational Intelligence (SCI, volume 345)
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Table of contents (9 chapters)
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Front Matter
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Back Matter
Keywords
About this book
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.
Reviews
From the reviews:
“The book provides an introduction to data fusion problems using support vector machines (SVMs). … The book is meant for researchers, scientists and engineers using SVMs, or other statistical learning methods, but it also may be used as a reference material for graduate courses in machine learning and data mining.” (Florin Gorunescu, Zentralblatt MATH, Vol. 1227, 2012)
Authors and Affiliations
Bibliographic Information
Book Title: Kernel-based Data Fusion for Machine Learning
Book Subtitle: Methods and Applications in Bioinformatics and Text Mining
Authors: Shi Yu, Léon-Charles Tranchevent, Bart Moor, Yves Moreau
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-642-19406-1
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer Berlin Heidelberg 2011
Hardcover ISBN: 978-3-642-19405-4Published: 26 March 2011
Softcover ISBN: 978-3-642-26751-2Published: 21 April 2013
eBook ISBN: 978-3-642-19406-1Published: 29 March 2011
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XIV, 214
Topics: Computational Intelligence, Artificial Intelligence, Computational Biology/Bioinformatics