18045009

Source:http://linkedlifedata.com/resource/pubmed/id/18045009

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Predicate	Object
rdf:type	pubmed:Citation
lifeskim:mentions	umls-concept:C0002045, umls-concept:C0035647, umls-concept:C1364142, umls-concept:C2699740
pubmed:issue	1
pubmed:dateCreated	2007-11-29
pubmed:abstractText	We describe a fast sequential minimal optimization (SMO) procedure for solving the dual optimization problem of the recently proposed potential support vector machine (P-SVM). The new SMO consists of a sequence of iteration steps in which the Lagrangian is optimized with respect to either one (single SMO) or two (dual SMO) of the Lagrange multipliers while keeping the other variables fixed. An efficient selection procedure for Lagrange multipliers is given, and two heuristics for improving the SMO procedure are described: block optimization and annealing of the regularization parameter epsilon. A comparison of the variants shows that the dual SMO, including block optimization and annealing, performs efficiently in terms of computation time. In contrast to standard support vector machines (SVMs), the P-SVM is applicable to arbitrary dyadic data sets, but benchmarks are provided against libSVM's epsilon-SVR and C-SVC implementations for problems that are also solvable by standard SVM methods. For those problems, computation time of the P-SVM is comparable to or somewhat higher than the standard SVM. The number of support vectors found by the P-SVM is usually much smaller for the same generalization performance.
pubmed:language	eng
pubmed:journal	http://linkedlifedata.com/resource/pubmed/journal/9426182
pubmed:citationSubset	IM
pubmed:status	MEDLINE
pubmed:month	Jan
pubmed:issn	0899-7667
pubmed:author	pubmed-author:HochreiterSeppS, pubmed-author:KnebelTilmanT, pubmed-author:ObermayerKlausK
pubmed:issnType	Print
pubmed:volume	20
pubmed:owner	NLM
pubmed:authorsComplete	Y
pubmed:pagination	271-87
pubmed:meshHeading	pubmed-meshheading:18045009-Algorithms, pubmed-meshheading:18045009-Artificial Intelligence, pubmed-meshheading:18045009-Computer Simulation, pubmed-meshheading:18045009-Neural Networks (Computer), pubmed-meshheading:18045009-Time Factors
pubmed:year	2008
pubmed:articleTitle	An SMO algorithm for the potential support vector machine.
pubmed:affiliation	Neural Information Processing Group, Fakultät IV, Technische Universität Berlin, 10587 Berlin, Germany. tk@cs.tu-berlin.de
pubmed:publicationType	Journal Article, Research Support, Non-U.S. Gov't