Support vector machine in the primal space based on\\ the ramp loss function\\[6mm

YUAN Yu-Ping; AN Zeng-Long

doi:2016.02.003

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Jul. 2016

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YUAN Yu-Ping, AN Zeng-Long. Support vector machine in the primal space based on\\ the ramp loss function\\[6mm[J]. Journal of East China Normal University (Natural Sciences), 2016, (2): 20-29. doi: 2016.02.003

Citation:

YUAN Yu-Ping, AN Zeng-Long. Support vector machine in the primal space based on\\ the ramp loss function\\[6mm[J]. Journal of East China Normal University (Natural Sciences), 2016, (2): 20-29. doi: 2016.02.003

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Support vector machine in the primal space based on\\ the ramp loss function\\[6mm

doi: 2016.02.003

YUAN Yu-Ping¹,
AN Zeng-Long^{2
,
,}

Received Date: 2015-03-04
Publish Date: 2016-03-25

Abstract

Abstract

Aiming at the problem of standard support vector machine being sensitive to the noise, a new method of support vector regression (SVR) machine based on dissymmetry quadratic and controlled-insensitive loss function is proposed. Using the concave and convex process optimization and the smooth technology algorithm, the problem of non-convex optimization is transformed into the problem of the continuous and twice differentiable convex optimization. Using the Amijo-Newton optimized algorithm of finite iteration termination, the established optimization model is solved, and the convergence of the algorithm is analyzed. The algorithm can not only keep the sparse nature of support vector, but also control the abnormal values of the training sample. The results of theexperiment showed that the support vector regression machine model proposed kept good generalization ability, and the model could fit better both the simulated data and the standard data. Compared with the standard support vector machine (SVM) model, the proposed model not only can reduce the effects of noise and outliers, but also has stronger robustness.
- support vector regression outliers loss function concave-convex procedure,

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References(1)

References

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