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EVOLUTIONARY OPTIMIZATION OF SUPPORT VECTOR MACHINES

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Date Issued:
2004
Abstract/Description:
Support vector machines are a relatively new approach for creating classifiers that have become increasingly popular in the machine learning community. They present several advantages over other methods like neural networks in areas like training speed, convergence, complexity control of the classifier, as well as a stronger mathematical background based on optimization and statistical learning theory. This thesis deals with the problem of model selection with support vector machines, that is, the problem of finding the optimal parameters that will improve the performance of the algorithm. It is shown that genetic algorithms provide an effective way to find the optimal parameters for support vector machines. The proposed algorithm is compared with a backpropagation Neural Network in a dataset that represents individual models for electronic commerce.
Title: EVOLUTIONARY OPTIMIZATION OF SUPPORT VECTOR MACHINES .
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Name(s): Gruber, Fred, Author
Rabelo, Luis, Committee Chair
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2004
Publisher: University of Central Florida
Language(s): English
Abstract/Description: Support vector machines are a relatively new approach for creating classifiers that have become increasingly popular in the machine learning community. They present several advantages over other methods like neural networks in areas like training speed, convergence, complexity control of the classifier, as well as a stronger mathematical background based on optimization and statistical learning theory. This thesis deals with the problem of model selection with support vector machines, that is, the problem of finding the optimal parameters that will improve the performance of the algorithm. It is shown that genetic algorithms provide an effective way to find the optimal parameters for support vector machines. The proposed algorithm is compared with a backpropagation Neural Network in a dataset that represents individual models for electronic commerce.
Identifier: CFE0000244 (IID), ucf:46251 (fedora)
Note(s): 2004-12-01
M.S.
Engineering and Computer Science, Department of Industrial Engineering and Management Systems
Masters
This record was generated from author submitted information.
Subject(s): support vector machines
neural networks
genetic algorithms
e-commerce
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0000244
Restrictions on Access: public
Host Institution: UCF

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