Research of Dimensionality Reduction for Fatigue Stress Concentration Factor Based on SVM by Linear Kernel

Jian Feng Yang; Zi Sheng Li; Gang Jiang; Jian Fei Chen

doi:10.4028/www.scientific.net/AMM.687-691.1649

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 687-691Research of Dimensionality Reduction for Fatigue...

Research of Dimensionality Reduction for Fatigue Stress Concentration Factor Based on SVM by Linear Kernel

Abstract:

We collected fatigue stress concentration factor and used Support Vector Machines (SVM) by linear kernel to reduce dimension processing. In order to research the way of dimensionality reduction for data, we also processed the sample of stress fatigue concentration factor to compare with Principal Component Analysis(PCA). The results showed that the sample is processed by linear kernel could improve efficiency to train by SVM again.

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Periodical:

Applied Mechanics and Materials (Volumes 687-691)

Pages:

1649-1652

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.687-691.1649

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Online since:

November 2014

Authors:

Jian Feng Yang*, Zi Sheng Li, Gang Jiang, Jian Fei Chen

Keywords:

Dimensionality Reduction, Figure, Linear Kernel, SVM

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* - Corresponding Author

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