Parameter Optimization of the SVM for Big Data

Open Access

Abstract:The traditional SVM parameter optimization use a wide range of traversal algorithm or some intelligent iterative algorithm, generally need to consume great deal of time, it is not applicable to optimization parameters of big data sets .To get around this ,This paper presents a strategy of stepwise optimize parameters based on the contour plots of cross-validation accuracy. Generate 25 parameter combinations uniformly, output the contour plots of cross-validation accuracy, then narrowing the optimal region of the parameters, proceeding stepwise optimizing parameter, until the optimal parameters were found. Finally, use a 13910*128 data set to verify the algorithm, compare with the traditional grid search algorithm, the new method not only greatly shorten the time of SVM parameters optimization, and it can find the better parameter than the traditional methods. This paper provides an effective solution to optimize SVM parameters especially for large data.
Keywords: support vector machine, SVM , parameter optimization , big data

Yunxiang Liu and Jiongjun Du

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School of Computer Science and Information Eng., Shanghai Institute of Technology, Shanghai, China

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Volume 2 Issue 2

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82 - 86

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