Genetic Algorithms For Support Vector Machine Optimization
Cross validation; Debris; Genetic algorithms; Support vector machines
Support vector machines are 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 paper 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 the NASA Shuttle Columbia Debris database.
IIE Annual Conference and Exposition 2005
Number of Pages
Article; Proceedings Paper
Source API URL
Gruber, Fred; Rabelo, Luis; and Sala-Diakanda, Serge, "Genetic Algorithms For Support Vector Machine Optimization" (2005). Scopus Export 2000s. 3275.