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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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 |
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Q Science (General) |
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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 |
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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 |
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1747829535958630400 |