Data Mining Using Support Vector Machines

The objectives of this work are; first to propose BbSVM and BmSVM as new boosting algorithms for enhancing the accuracy and performance of common SVM. Second,to show the robustness of various kind of kernels for BbSVM and BmSVM classifiers,and a comparison of different constructing methods for Multi...

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Bibliographic Details
Main Author:	Chamasemani, Fereshteh Falah
Format:	Thesis
Published:	2011
Subjects:	Q Science (General)
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id	my-mmu-ep.3653
record_format	uketd_dc
spelling	my-mmu-ep.36532012-12-03T01:31:11Z Data Mining Using Support Vector Machines 2011-06 Chamasemani, Fereshteh Falah Q Science (General) The objectives of this work are; first to propose BbSVM and BmSVM as new boosting algorithms for enhancing the accuracy and performance of common SVM. Second,to show the robustness of various kind of kernels for BbSVM and BmSVM classifiers,and a comparison of different constructing methods for Multi-class SVM,like One-Against-All,One-Against-One Binary Tree and Directed Acyclic Graph. 2011-06 Thesis http://shdl.mmu.edu.my/3653/ http://vlib.mmu.edu.my/diglib/login/dlusr/login.php masters Multimedia University Faculty of Information Technology
institution	Multimedia University
collection	MMU Institutional Repository
topic	Q Science (General)
spellingShingle	Q Science (General) Chamasemani, Fereshteh Falah Data Mining Using Support Vector Machines
description	The objectives of this work are; first to propose BbSVM and BmSVM as new boosting algorithms for enhancing the accuracy and performance of common SVM. Second,to show the robustness of various kind of kernels for BbSVM and BmSVM classifiers,and a comparison of different constructing methods for Multi-class SVM,like One-Against-All,One-Against-One Binary Tree and Directed Acyclic Graph.
format	Thesis
qualification_level	Master's degree
author	Chamasemani, Fereshteh Falah
author_facet	Chamasemani, Fereshteh Falah
author_sort	Chamasemani, Fereshteh Falah
title	Data Mining Using Support Vector Machines
title_short	Data Mining Using Support Vector Machines
title_full	Data Mining Using Support Vector Machines
title_fullStr	Data Mining Using Support Vector Machines
title_full_unstemmed	Data Mining Using Support Vector Machines
title_sort	data mining using support vector machines
granting_institution	Multimedia University
granting_department	Faculty of Information Technology
publishDate	2011
_version_	1747829535958630400

Data Mining Using Support Vector Machines

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