NDLI: Model selection of RBF kernel for C-SVM based on genetic algorithm and multithreading

Content Provider	IEEE Xplore Digital Library
Author	Guo-You Shi Shuang Liu
Copyright Year	2012
Description	Author affiliation: School of Computer Science & Engineering, Dalian Nationalities University, 116026, China (Shuang Liu) \|\| College of Navigation, Dalian Maritime University, 116026, China (Guo-You Shi)
Abstract	Generalization performance of support vector machines depends on optimal selection of parameter values. But training the best parameters for C-Support Vector Machines (C-SVM) classifier with RBF kernel is time-consuming. We can hardly finish training process for large data sets with traditional methods. Multithreading as a widespread programming and execution model allows multiple threads to exist within the context of a single process, which has been widely applied in data processing and analyzing. In this paper, we studied how to adopt genetic algorithm and multithreading model to complete optimal model selection of C-SVM classifier with RBF kernel. This new approach not only chooses global parameters, but also saves training time based on parallel computing process. Experimental results show the efficiency and feasibility of new approach.
Starting Page	382
Ending Page	386
File Size	402454
Page Count	5
File Format	PDF
ISBN	9781467314848
ISSN	2160133X
e-ISBN	9781467314879
DOI	10.1109/ICMLC.2012.6358944
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Abstracts Vectors Training Biological cells Training data Testing Classifier Support vector machines RBF kernel Model selection Multithreading
Content Type	Text
Resource Type	Article

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