Expediting model selection for support vector machines based on an advanced data reduction algorithm

Authors:
Yu-Yen Ou;Guan-Hau Chen;Yen-Jen Oyang
Affiliations:
Graduate School of Biotechnology and Bioinformatics, Department of Computer Science and Engineering, Yuan-Ze University, Chung-Li, Taiwan;Graduate School of Biotechnology and Bioinformatics, Department of Computer Science and Engineering, Yuan-Ze University, Chung-Li, Taiwan;Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
Venue:
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Year:
2006

Citing 8
Cited 1

Support-Vector Networks

Machine Learning
Reduction Techniques for Instance-BasedLearning Algorithms

Machine Learning
Alpha seeding for support vector machines

Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Multidimensional binary search trees used for associative searching

Communications of the ACM
Choosing Multiple Parameters for Support Vector Machines

Machine Learning
A comparison of methods for multiclass support vector machines

IEEE Transactions on Neural Networks
Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms

IEEE Transactions on Neural Networks
Data classification with radial basis function networks based on a novel kernel density estimation algorithm

IEEE Transactions on Neural Networks

On the fusion of polynomial kernels for support vector classifiers

IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

In recent years, the support vector machine (SVM) has been extensively applied to deal with various data classification problems. However, it has also been observed that, for some datasets, the classification accuracy delivered by the SVM is very sensitive to how the cost parameter and the kernel parameters are set. As a result, the user may need to conduct extensive cross validation in order to figure out the optimal parameter setting. How to expedite the model selection process of the SVM has attracted a high degree of attention in the machine learning research community in recent years. This paper proposes an advanced data reduction algorithm aimed at expediting the model selection process of the SVM. Experimental results reveal that the proposed mechanism is able to deliver a speedup of over 70 times without causing meaningful side effects and compares favorably with the alternative approaches.