Parallel sequential minimal optimization for the training of support vector machines

Authors:
L. J. Cao;S. S. Keerthi;Chong-Jin Ong;J. Q. Zhang;U. Periyathamby;Xiu Ju Fu;H. P. Lee
Affiliations:
Dept. of Financial Studies, Fudan Univ., Shanghai, China;-;-;-;-;-;-
Venue:
IEEE Transactions on Neural Networks
Year:
2006

Citing 0
Cited 14

Fast support vector machine training and classification on graphics processors

Proceedings of the 25th international conference on Machine learning
Incorporating Domain Knowledge into a Min-Max Modular Support Vector Machine for Protein Subcellular Localization

Neural Information Processing
A Parallel Implementation of Error Correction SVM with Applications to Face Recognition

ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Fast support vector machines for continuous data

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Parallel multiclass classification using SVMs on GPUs

Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units
Improved computation for Levenberg-Marquardt training

IEEE Transactions on Neural Networks
A MapReduce-based distributed SVM algorithm for automatic image annotation

Computers & Mathematics with Applications
Letters: Fast training of support vector machines on the Cell processor

Neurocomputing
A novel distributed machine learning method for classification: parallel covering algorithm

RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Performance evaluation of hybrid implementation of support vector machine

IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Multi-threaded support vector machines for pattern recognition

ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Parallel multitask cross validation for Support Vector Machine using GPU

Journal of Parallel and Distributed Computing
An efficient classification approach for large-scale mobile ubiquitous computing

Information Sciences: an International Journal
An ontology enhanced parallel SVM for scalable spam filter training

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Sequential minimal optimization (SMO) is one popular algorithm for training support vector machine (SVM), but it still requires a large amount of computation time for solving large size problems. This paper proposes one parallel implementation of SMO for training SVM. The parallel SMO is developed using message passing interface (MPI). Specifically, the parallel SMO first partitions the entire training data set into smaller subsets and then simultaneously runs multiple CPU processors to deal with each of the partitioned data sets. Experiments show that there is great speedup on the adult data set and the Mixing National Institute of Standard and Technology (MNIST) data set when many processors are used. There are also satisfactory results on the Web data set.