… In this article we introduce a large scale training … transductive SVMs with an empirical
scaling of (L+U)2, which involves training a sequence of typically 1-10 conventional convex SVM …

… But in contrast to the linear case, both alternatives do not scale well with large-scale data. …
to other related transductive SVM methods, but it is faster than other transductive learning …

… learning process in a transductive learning setting. Since the labeled data can be noisy
in large-scale image retrieval applications, we introduce a more robust transductive SVM (TSVM) …

… Intuitively, we would expect transductive learning to yield improvements when the training
… transductive support vector machine is addressed to extend Joachims’ transductive SVM to …

… Because of this, the base line SVM method and the transductive SVM method have higher
accuracy as compared to those reported in the literature when default parameter values are …

… for a variant of Transductive SVM [5] designed for linear semi-supervised classification
on large, sparse datasets. As compared to currently used dual techniques (eg in the SVM …

… data as in Transductive Support Vector Machines (TSVM) so … In this article we introduced
a large scale nonlinear method … supervised learning: transduction and neighborhoodbased (…

… Transductive support vector machines (TSVM) has been widely used as a means of
treating … First, TSVM performs no worse than its supervised counterpart SVM when tuning is …

… learning process in a transductive learning setting. Since the labeled data can be noisy
in large-scale image retrieval applications, we introduce a more robust transductive SVM (TSVM) …

… In the above framework, to address the issue of the available biased/poor training set, a
transductive SVM approach has been proposed by involving unlabeled samples into the training …