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007			cr nn 008mamaa
008			110328s2011 gw | s |||| 0|eng d
020			_a9783642194061 _9978-3-642-19406-1
040			_cMX-MeUAM
050		4	_aQ342
082	0	4	_a006.3 _223
100	1		_aYu, Shi. _eauthor.
245	1	0	_aKernel-based Data Fusion for Machine Learning _h[recurso electrónico] : _bMethods and Applications in Bioinformatics and Text Mining / _cby Shi Yu, Léon-Charles Tranchevent, Bart Moor, Yves Moreau.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2011.
300			_aXIV, 214 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aStudies in Computational Intelligence, _x1860-949X ; _v345
505	0		_aIntroduction -- 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.
520			_aData 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.
650		0	_aEngineering.
650		0	_aArtificial intelligence.
650		0	_aBioinformatics.
650	1	4	_aEngineering.
650	2	4	_aComputational Intelligence.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aComputational Biology/Bioinformatics.
700	1		_aTranchevent, Léon-Charles. _eauthor.
700	1		_aMoor, Bart. _eauthor.
700	1		_aMoreau, Yves. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642194054
830		0	_aStudies in Computational Intelligence, _x1860-949X ; _v345
856	4	0	_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-19406-1
596			_a19
942			_cLIBRO_ELEC
999			_c203717 _d203717