A hierarchical and parallel method for training support vector machines

Authors:
Yimin Wen;Baoliang Lu
Affiliations:
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Year:
2005

Citing 4
Cited 3

Making large-scale support vector machine learning practical

Advances in kernel methods
Handling concept drifts in incremental learning with support vector machines

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
The Bayesian Committee Support Vector Machine

ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Task decomposition and module combination based on class relations: a modular neural network for pattern classification

IEEE Transactions on Neural Networks

A Confident Majority Voting Strategy for Parallel and Modular Support Vector Machines

ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
A resource allocating neural network based approach for detecting end-to-end network performance anomaly

ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Parallel approaches to machine learning-A comprehensive survey

Journal of Parallel and Distributed Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In order to handle large-scale pattern classification problems, various sequential and parallel classification methods have been developed according to the divide-and-conquer principle. However, existing sequential methods need long training time, and some of parallel methods lead to generalization accuracy decreasing and the number of support vectors increasing. In this paper, we propose a novel hierarchical and parallel method for training support vector machines. The simulation results indicate that our method can not only speed up training but also reduce the number of support vectors while maintaining the generalization accuracy.