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
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
A dual coordinate descent method for large-scale linear SVM
Proceedings of the 25th international conference on Machine learning
Semi-Supervised Learning
How about utilizing ordinal information from the distribution of unlabeled data
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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Semi-supervised learning has been successfully applied to many fields such as knowledge management, information retrieval and data mining as it can utilize both labeled and unlabeled data. In this paper, we propose a general semi-supervised framework for multi-class categorization. Many classical supervised and semi-supervised method dealing with binary classification or multi-class classification including the standard regularization and the manifold regularization can be viewed as special cases of this framework. Based on this framework, we propose a novel method called multi-class unlabeled constrained SVM(MCUCSVM) and its special case: multi-class Laplacian SVM(MCLapSVM). We then put forward a general kernel version semi-supervised dual coordinate descent algorithm to efficiently solve MCUCSVM and makes it more applicable to problems with large number of classes and large scale labeled data. Both rigorous theory and promising experimental results on four real datasets show the great performance and remarkable efficiency of MCUCSVM and MCLapSVM.