NDLI: Stochastic optimization for learning Non-Convex Linear Support Vector Machines

Content Provider	IEEE Xplore Digital Library
Author	Jifei Chen Jiabao Wang Yafei Zhang Jianjiang Lu Yang Li
Copyright Year	2012
Description	Author affiliation: Institute of Command Automation, PLA University of Science and Technology, Nanjing 210007, China (Jifei Chen; Jiabao Wang; Yafei Zhang; Jianjiang Lu; Yang Li)
Abstract	In this paper, a fast optimization algorithm was proposed to learn the Non-Convex Linear Support Vector Machines (LSVM-NC) based on stochastic optimization, in which the non-convex function, Ramp Loss, was used to suppress the influence of noisy data in the case of large-scale learning problems. As for solving the non-convex linear SVMs, the traditional methods make use of the ConCave-Convex Procedure (CCCP) based on the Sequential Minimal Optimization (SMO) algorithm from dual, which is a time-consuming process and impractical for learning large-scale problems. To tackle this, we resorted to CCCP based on Stochastic Gradient Descent (SGD) algorithm from primal, and experimental results proved that our method could reduce the training time largely and improve the generalization performance.
Starting Page	35
Ending Page	39
File Size	109664
Page Count	5
File Format	PDF
ISBN	9781467313315
e-ISBN	9781467313322
DOI	10.1109/ICADE.2012.6330094
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-27
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Support vector machines Machine learning algorithms Large-Scale Machine Learning Stochastic processes Stochastic Gradient Descent Machine learning Non-Convex Linear Support Vector Machines Optimization Testing
Content Type	Text
Resource Type	Article

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