Transductive support vector machines using simulated annealing

Authors:
Fan Sun;Maosong Sun
Affiliations:
State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science & Technology, Tsinghua University, Beijing, China;State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science & Technology, Tsinghua University, Beijing, China
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Year:
2005

Citing 4
Cited 0

Support-Vector Networks

Machine Learning
Transductive Inference for Text Classification using Support Vector Machines

ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)
Learning by transduction

UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Transductive inference estimates classification function at samples within the test data using information from both the training and the test data set. In this paper, a new algorithm of transductive support vector machine is proposed to improve Joachims’ transductive SVM to handle various data distributions. Simulated annealing heuristic is used to solve the combinatorial optimization problem of TSVM, in order to avoid the problems of having to estimate the ratio of positive/negative samples and local optimum. The experimental result shows that TSVM-SA algorithm outperforms Joachims’ TSVM, especially when there is a significant deviation between the distribution of training and test data.