Title :
Learning with progressive transductive Support Vector Machine
Author :
Chen, Yisong ; Wang, Guoping ; Dong, Shihai
Author_Institution :
HCI & Multimedia Lab., Peking Univ., Beijing, China
Abstract :
Support Vector Machine (SVM) is a new learning method developed in recent years based on the foundations of statistical learning theory. By taking a transductive approach instead of an inductive one in support vector classifiers, the test set can be used as an additional source of information about margins. Intuitively, we would expect transductive learning to yield improvements when the training sets are small or when there is a significant deviation between the training and working set subsamples of the total population. In this paper, a progressive transductive support vector machine is addressed to extend Joachims´ Transductive SVM to handle different class distributions. It solves the problem of having to estimate the ratio of positive/negative examples from the working set. The experimental results show that the algorithm is very promising.
Keywords :
learning automata; learning by example; pattern recognition; SVM; pattern recognition; progressive transductive support vector machine; statistical learning; support vector classifiers; support vector machine; training sets; transductive inference; transductive learning; Human computer interaction; Inference algorithms; Iterative algorithms; Labeling; Laboratories; Machine learning; Risk management; Support vector machine classification; Support vector machines; Testing;
Conference_Titel :
Data Mining, 2002. ICDM 2003. Proceedings. 2002 IEEE International Conference on
Print_ISBN :
0-7695-1754-4
DOI :
10.1109/ICDM.2002.1183887