Title :
The research of the parallel SMO algorithm for solving SVM
Author :
Peng, Peng ; Ma, Qian-li ; Hong, Lei-ming
Author_Institution :
South China Univ. of Technol., Guangzhou, China
Abstract :
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.
Keywords :
algorithm theory; learning (artificial intelligence); minimisation; support vector machines; buffer; learning algorithm; master CPU; parallel SMO algorithm; primitive training set; sequential minimal optimisation; serial SMO; slave CPU; support vector machine algorithm; Cybernetics; Kernel; Large-scale systems; Machine learning; Machine learning algorithms; Master-slave; Pattern recognition; Probability density function; Support vector machine classification; Support vector machines; Learning algorithm; Parallel SMO; Support Vector Machine;
Conference_Titel :
Machine Learning and Cybernetics, 2009 International Conference on
Conference_Location :
Baoding
Print_ISBN :
978-1-4244-3702-3
Electronic_ISBN :
978-1-4244-3703-0
DOI :
10.1109/ICMLC.2009.5212348
