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Books > Computing & IT > Applications of computing > Artificial intelligence
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Kernel-based Data Fusion for Machine Learning - Methods and Applications in Bioinformatics and Text Mining (Paperback, 2011 ed.)
Loot Price: R4,673
Discovery Miles 46 730
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Kernel-based Data Fusion for Machine Learning - Methods and Applications in Bioinformatics and Text Mining (Paperback, 2011 ed.)
Series: Studies in Computational Intelligence, 345
Expected to ship within 18 - 22 working days
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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.
General
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