An analysis of linear weight updating algorithms for text classification

Gkiokas, A; Demiros, I; Piperidis, S

dc.contributor.author	Gkiokas, A	en
dc.contributor.author	Demiros, I	en
dc.contributor.author	Piperidis, S	en
dc.date.accessioned	2014-03-01T02:43:54Z
dc.date.available	2014-03-01T02:43:54Z
dc.date.issued	2006	en
dc.identifier.issn	0302-9743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31545
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.other	Exponential multiplication function	en
dc.subject.other	Surface parameters	en
dc.subject.other	Text classification	en
dc.subject.other	Weight updating algorithms	en
dc.subject.other	Algorithms	en
dc.subject.other	Computation theory	en
dc.subject.other	Function evaluation	en
dc.subject.other	Learning systems	en
dc.subject.other	Parameter estimation	en
dc.subject.other	Problem solving	en
dc.subject.other	Text processing	en
dc.subject.other	Classification (of information)	en
dc.title	An analysis of linear weight updating algorithms for text classification	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/11752912_56	en
heal.identifier.secondary	http://dx.doi.org/10.1007/11752912_56	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	This paper addresses the problem of text classification in high dimensionality spaces by applying linear weight updating classifiers that have been highly studied in the domain of machine learning. Our experimental results are based on the Winnow family of algorithms that are simple to implement and efficient in terms of computation time and storage requirements. We applied an exponential multiplication function to weight updates and we experimentally calculated the optimal values of the learning rate and the separating surface parameters. Our results are at the level of the best results that were reported on the family of linear algorithms and perform nearly as well as the top performing methodologies in the literature. © Springer-Verlag Berlin Heidelberg 2006.	en
heal.publisher	SPRINGER-VERLAG BERLIN	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
heal.bookName	LECTURE NOTES IN COMPUTER SCIENCE	en
dc.identifier.doi	10.1007/11752912_56	en
dc.identifier.isi	ISI:000238053100054	en
dc.identifier.volume	3955 LNAI	en
dc.identifier.spage	508	en
dc.identifier.epage	511	en