Weka

dc.contributor	Virginia Tech. Digital Library Research Laboratory	en
dc.contributor	Virginia Tech. Department of Computer Science	en
dc.contributor.author	Peddi, Bhanu	en
dc.contributor.author	Xiong, Huijun	en
dc.contributor.author	ElSherbiny, Noha	en
dc.contributor.department	Digital Library Research Laboratory	en
dc.contributor.department	Computer Science	en
dc.contributor.editor	Fox, Edward A.	en
dc.date.accessioned	2015-05-22T14:18:55Z	en
dc.date.available	2015-05-22T14:18:55Z	en
dc.date.issued	2010-12-10	en
dc.description.abstract	This module stresses the methods of text classification used in information retrieval. We focus on the usage of Weka, a data mining toolkit, in data processing with three classification algorithms: Naive Bayes [1], k Nearest Neighbor [2], and Support Vector Machine [3]) mentioned in the textbook [7].	en
dc.description.notes	CS 5604: Information Storage and Retrieval	en
dc.format.extent	7 pages	en
dc.format.mimetype	application/pdf	en
dc.identifier.uri	http://hdl.handle.net/10919/52533	en
dc.identifier.url	http://curric.dlib.vt.edu/modDev/package_modules/FinalModule-Team5-Weka.pdf	en
dc.language.iso	en_US	en
dc.relation.ispartofseries	Digital Library Curriculum Project	en
dc.rights	In Copyright	en
dc.rights.uri	http://rightsstatements.org/vocab/InC/1.0/	en
dc.subject	Computer science	en
dc.subject	Digital libraries	en
dc.subject	Weka	en
dc.subject	Data mining	en
dc.title	Weka	en
dc.type	Learning object	en
dc.type.dcmitype	Text	en

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