Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
The Journal of Machine Learning Research
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
ICML '05 Proceedings of the 22nd international conference on Machine learning
Large scale semi-supervised linear SVMs
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Semi-Supervised Learning
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Usually, we can use a classification or clustering machine learning algorithm to manage knowledge and information retrieval. If we have a small size of known information with a large scale of unknown data, a semi-supervised learning (SSL) algorithm is often preferred. Under the cluster or manifold assumption, usually, the larger amount of unlabeled data are used for learning, the bigger gains of the SSL approaches are achieved. In the paper, we adopt the graph-based SSL algorithm to solve the problem. However the graph-based SSL algorithms are unable to be learnt with large-scale unlabeled samples and originally can only work in a transductive setting. In the paper, we propose a scalable graph-based SSL algorithm to attack the problems aforementioned by Gaussian mixture model label propagation. Experiments conducted on the real dataset illustrate the effectiveness of the proposed algorithm.