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Title of Journal: Optim Lett

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Abbravation: Optimization Letters

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Springer Berlin Heidelberg

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DOI

10.1002/ima.20032

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1862-4480

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Stochastic subgradient descent method for largesc

Authors: Ximing Wang Neng Fan Panos M Pardalos
Publish Date: 2016/03/15
Volume: 11, Issue: 5, Pages: 1013-1024
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Abstract

Robust chanceconstrained Support Vector Machines SVM with secondorder moment information can be reformulated into equivalent and tractable Semidefinite Programming SDP and Second Order Cone Programming SOCP models However practical applications involve processing largescale data sets For the reformulated SDP and SOCP models existed solvers by primaldual interior method do not have enough computational efficiency This paper studies the stochastic subgradient descent method and algorithms to solve robust chanceconstrained SVM on largescale data sets Numerical experiments are performed to show the efficiency of the proposed approaches The result of this paper breaks the computational limitation and expands the application of robust chanceconstrained SVM

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