Title :
Model selection of RBF kernel for C-SVM based on genetic algorithm and multithreading
Author :
Guo-You Shi ; Shuang Liu
Author_Institution :
Coll. of Navig., Dalian Maritime Univ., Dalian, China
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.
Keywords :
genetic algorithms; multi-threading; pattern classification; radial basis function networks; support vector machines; C-SVM classifier; C-support vector machine classifier; RBF kernel; data analysis; data processing; execution model; genetic algorithm; global parameters; multithreading model; optimal model selection; parallel computing; programming model; Abstracts; Accuracy; Biological cells; Testing; Training; Training data; Vectors; Classifier; Model selection; Multithreading; RBF kernel; Support vector machines;
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2012 International Conference on
Conference_Location :
Xian
Print_ISBN :
978-1-4673-1484-8
DOI :
10.1109/ICMLC.2012.6358944