Publication: Genetic Algorithm Optimization of Dynamic Support Vector Regression
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2009
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Milnes, Thomas Bradford, Christopher Thorpe, and Avi Pfeffer. 2009. Genetic Algorithm Optimization of Dynamic Support Vector Regression. Harvard Computer Science Group Technical Report TR-08-09.
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Abstract
We show that genetic algorithms (GA) find optimized dynamic support vector machines (DSVMs) more efficiently than the grid search (GS) optimization approach. In addition, we show that GA-DSVMs find extremely low-error solutions for a number of oft-cited benchmarks. Unlike standard support vector machines, DSVMs account for the fact that data further back in a time series are generally less predictive than more-recent data. In order to tune the discounting factors, DSVMs require two new free parameters for a total of five. Because of the five free parameters, traditional GS optimization becomes intractable for even modest grid resolutions. GA optimization finds better results while using fewer computational resources.
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