The research of the parallel SMO algorithm for solving SVM

In order to improve solving support vector machine algorithm, an improved learning algorithm of the parallel SMO is proposed. According to this algorithm, the master CPU averagely distributes primitive training set to slave CPUs so that they can almost independently run serial SMO on their respectiv...

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Bibliographic Details
Published in2009 International Conference on Machine Learning and Cybernetics Vol. 3; pp. 1271 - 1274
Main Authors Peng Peng, Qian-Li Ma, Lei-Ming Hong
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
Subjects
Cybernetics
Kernel
Large-scale systems
Learning algorithm
Machine learning
Machine learning algorithms
Master-slave
Parallel SMO
Pattern recognition
Probability density function
Support Vector Machine
Support vector machine classification
Support vector machines
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Summary:In order to improve solving support vector machine algorithm, an improved learning algorithm of the parallel SMO is proposed. According to this algorithm, the master CPU averagely distributes primitive training set to slave CPUs so that they can almost independently run serial SMO on their respective training set. As it adopts the strategies of buffer and shrink, the speed of the parallel training algorithm is increased, which is showed in the experiments of parallel SMO based on the dataset of MNIST. The experiments indicate that the parallel SMO algorithm has good performance in solving largescale SVM.
ISBN:9781424437023
1424437024
ISSN:2160-133X
DOI:10.1109/ICMLC.2009.5212348