The nature of statistical learning theory
The nature of statistical learning theory
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A Fast and Accurate Progressive Algorithm for Training Transductive SVMs
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Learning with Sequential Minimal Transductive Support Vector Machine
FAW '09 Proceedings of the 3d International Workshop on Frontiers in Algorithmics
Semi-supervised SVMs for classification with unknown class proportions and a small labeled dataset
Proceedings of the 20th ACM international conference on Information and knowledge management
| Hi-index | 0.10 |
The transductive support vector machine (TSVM) is the transductive inference of the support vector machine. The TSVM utilizes the information carried by the unlabeled samples for classification and acquires better classification performance than the regular support vector machine (SVM). As effective as the TSVM is, it still has obvious deficiency: The number of positive samples must be appointed before training and it is not changed during the training phase. This deficiency is caused by the pair-wise exchanging criterion used in the TSVM. In this paper, we propose a new transductive training algorithm by substituting the pair-wise exchanging criterion with the individually judging and changing criterion. Experimental results show that the new method releases the restriction of the appointment of the number of positive samples beforehand and improves the adaptability of the TSVM.