16764511

Source:http://linkedlifedata.com/resource/pubmed/id/16764511

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Predicate	Object
rdf:type	pubmed:Citation
lifeskim:mentions	umls-concept:C1511726, umls-concept:C2699740
pubmed:issue	6
pubmed:dateCreated	2006-6-12
pubmed:abstractText	We describe a new technique for the analysis of dyadic data, where two sets of objects (row and column objects) are characterized by a matrix of numerical values that describe their mutual relationships. The new technique, called potential support vector machine (P-SVM), is a large-margin method for the construction of classifiers and regression functions for the column objects. Contrary to standard support vector machine approaches, the P-SVM minimizes a scale-invariant capacity measure and requires a new set of constraints. As a result, the P-SVM method leads to a usually sparse expansion of the classification and regression functions in terms of the row rather than the column objects and can handle data and kernel matrices that are neither positive definite nor square. We then describe two complementary regularization schemes. The first scheme improves generalization performance for classification and regression tasks; the second scheme leads to the selection of a small, informative set of row support objects and can be applied to feature selection. Benchmarks for classification, regression, and feature selection tasks are performed with toy data as well as with several real-world data sets. The results show that the new method is at least competitive with but often performs better than the benchmarked standard methods for standard vectorial as well as true dyadic data sets. In addition, a theoretical justification is provided for the new approach.
pubmed:language	eng
pubmed:journal	http://linkedlifedata.com/resource/pubmed/journal/9426182
pubmed:citationSubset	IM
pubmed:status	MEDLINE
pubmed:month	Jun
pubmed:issn	0899-7667
pubmed:author	pubmed-author:HochreiterSeppS, pubmed-author:ObermayerKlausK
pubmed:issnType	Print
pubmed:volume	18
pubmed:owner	NLM
pubmed:authorsComplete	Y
pubmed:pagination	1472-510
pubmed:dateRevised	2006-11-15
pubmed:meshHeading	pubmed-meshheading:16764511-Artificial Intelligence, pubmed-meshheading:16764511-Databases, Genetic, pubmed-meshheading:16764511-Databases, Protein, pubmed-meshheading:16764511-Models, Theoretical, pubmed-meshheading:16764511-Oligonucleotide Array Sequence Analysis
pubmed:year	2006
pubmed:articleTitle	Support vector machines for dyadic data.
pubmed:affiliation	Department of Electrical Engineering and Computer Science, Technische Universität, Berlin, Germany. hochreit@cs.tu-berlin.de
pubmed:publicationType	Journal Article, Research Support, Non-U.S. Gov't