Arabic part-of-speech tagger based support vectors machines

Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/395132

Full metadata record

DC Field	Value	Language
dc.contributor.author	Jabar Hassan Yousif	-
dc.contributor.author	Tengku Mohd Tengku Sembok	-
dc.date.accessioned	2023-06-15T07:55:41Z	-
dc.date.available	2023-06-15T07:55:41Z	-
dc.identifier.other	ukmvital:123083	-
dc.identifier.uri	http://ptsldigitalv2.ukm.my:8080/jspui/handle/123456789/395132	-
dc.description.abstract	Support Vector Machines (SVMs) and related kernel methods have become widely known tools for text mining tasks such as classification and regression. The Arabic part of speech (POS) based support vectors machine is designed and implemented. The NeuroSolutions software is used to adopt and learn the proposed tagger. The Radial basis functions (RBFs) is used as a linear function approximator. The experiments has give an evinced that the SVMS tagger is accurate of (99.99%), has low processing time, and use a little a mount of data at training phase .	-
dc.language.iso	eng	-
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE),Piscataway, US	-
dc.subject	Arabic part-of-speech	-
dc.subject	Vectors machines	-
dc.title	Arabic part-of-speech tagger based support vectors machines	-
dc.type	Seminar Papers	-
dc.format.pages	7	-
dc.identifier.callno	T58.5.C634 2008 kat sem j.3	-
dc.contributor.conferencename	International Symposium on Information Technology	-
dc.coverage.conferencelocation	Kuala Lumpur Convention Centre	-
dc.date.conferencedate	26/08/2008	-
Appears in Collections:	Seminar Papers/ Proceedings / Kertas Kerja Seminar/ Prosiding

Files in This Item:

There are no files associated with this item.