Cooperative clustering for training SVMs

Authors:
Shengfeng Tian;Shaomin Mu;Chuanhuan Yin
Affiliations:
School of Computer and Information Technology, Beijing Jiaotong University, Beijing, P.R. China;School of Computer and Information Technology, Beijing Jiaotong University, Beijing, P.R. China;School of Computer and Information Technology, Beijing Jiaotong University, Beijing, P.R. China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

Citing 5
Cited 1

A training algorithm for optimal margin classifiers

COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Least Squares Support Vector Machine Classifiers

Neural Processing Letters
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Exact simplification of support vector solutions

The Journal of Machine Learning Research
A study on reduced support vector machines

IEEE Transactions on Neural Networks

A Fast Multiclass Classification Algorithm Based on Cooperative Clustering

Neural Processing Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

Support vector machines are currently very popular approaches to supervised learning. Unfortunately, the computational load for training and classification procedures increases drastically with size of the training data set. In this paper, a method called cooperative clustering is proposed. With this procedure, the set of data points with pre-determined size near the border of two classes is determined. This small set of data points is taken as the set of support vectors. The training of support vector machine is performed on this set of data points. With this approach, training efficiency and classification efficiency are achieved with small effects on generalization performance. This approach can also be used to reduce the number of support vectors in regression problems.