Evolutionary Optimization Of Support Vector Machines

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.

Author dc:creator

• Gruber, Fred

Contributors dc:contributor

• Rabelo, Luis

• support vector machines
• neural networks
• genetic algorithms
• e-commerce
• Engineering

Language dc:language

English

Identifier

CFE0000244

OAI identifier oai:identifier

oai:stars.library.ucf.edu:etd-1189