Kernel-based Data Fusion for Machine Learning

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Methods and Applications in Bioinformatics and Text Mining, Studies in Computational Intelligence 345

ISBN: 3642267513
ISBN 13: 9783642267512
Verlag: Springer Verlag GmbH
Umfang: xiv, 214 S., 41 s/w Illustr., 2 farbige Illustr.
Erscheinungsdatum: 21.04.2013
Weitere Autoren: Yu, Shi/Tranchevent, Léon-Charles/Moor, Bart et al
Auflage: 1/2013
Produktform: Kartoniert
Einband: KT

Data fusion problems arise in many different fields. This book provides a specific introduction to solve data fusion problems using support vector machines. The reader will require a good knowledge of data mining, machine learning and linear algebra.

Artikelnummer: 4624240 Kategorie:

Beschreibung

InhaltsangabeIntroduction.- Rayleigh quotient-type problems in machine learning.- Ln-norm Multiple Kernel Learning and Least Squares Support VectorMachines.- Optimized data fusion for kernel k-means Clustering.- Multi-view text mining for disease gene prioritization and clustering.- Optimized data fusion for k-means Laplacian Clustering.- Weighted Multiple Kernel Canonical Correlation.- Cross-species candidate gene prioritization with MerKator.- Conclusion.

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