NDLI: Parallelized incremental support vector machines based on MapReduce and Bagging technique

Content Provider	IEEE Xplore Digital Library
Author	Jun Zhao Zhu Liang Yong Yang
Copyright Year	2012
Description	Author affiliation: Institute of Computer Science & Technology, Chongqing University of Posts and Telecommunications, Chongqing, CO 400065 China (Jun Zhao; Zhu Liang; Yong Yang)
Abstract	One of the mainstream research fields in learning from empirical data by support vector machines (SVM) is an implementation of the incremental learning schemes when the training dataset is huge. Moreover, the challenge of applying incremental SVMs on huge data sets comes from the fact that the amount of computer memory and learning time required along with the amount of dataset increased. In this paper, a parallelized incremental SVM (PISVM) learning algorithm for huge data is proposed. The parallel programming model of MapReduce is introduced and combined with incremental learning method. Each individual SVM is independently trained based on the randomly selected training samples via bootstrap technique, and learns from the new samples independently also. The final decision is made according to the majority voting by all SVMs. Experiment results on UCI standard data sets show that the training time can be reduced and the accuracy can be ensured for the proposed algorithm.
Starting Page	297
Ending Page	301
File Size	599075
Page Count	5
File Format	PDF
ISBN	9781457703430
e-ISBN	9781457703454
DOI	10.1109/ICIST.2012.6221655
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-03-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Computers Accuracy Machine learning Bagging Standards
Content Type	Text
Resource Type	Article

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