Keywords

support vector machines, neural networks, genetic algorithms, e-commerce

Abstract

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.

Notes

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Graduation Date

2004

Semester

Fall

Advisor

Rabelo, Luis

Degree

Master of Science (M.S.)

College

College of Engineering and Computer Science

Department

Industrial Engineering and Management Systems

Degree Program

Industrial Engineering and Management Systems

Format

application/pdf

Identifier

CFE0000244

URL

http://purl.fcla.edu/fcla/etd/CFE0000244

Language

English

Release Date

December 2004

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

Restricted to the UCF community until December 2004; it will then be open access.

Included in

Engineering Commons

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