Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
A Second-Order Perceptron Algorithm
SIAM Journal on Computing
Video suggestion and discovery for youtube: taking random walks through the view graph
Proceedings of the 17th international conference on World Wide Web
Confidence-weighted linear classification
Proceedings of the 25th international conference on Machine learning
Soft-supervised learning for text classification
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
New Regularized Algorithms for Transductive Learning
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Multi-class confidence weighted algorithms
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Semi-Supervised Learning
Experiments in graph-based semi-supervised learning methods for class-instance acquisition
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Semi-Supervised Learning with Measure Propagation
The Journal of Machine Learning Research
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We present a new multi-class graph-based transduction algorithm. Examples are associated with vertices in an undirected weighted graph and edge weights correspond to a similarity measure between examples. Typical algorithms in such a setting perform label propagation between neighbours, ignoring the quality, or estimated quality, in the labeling of various nodes. We introduce an additional quantity of confidence in label assignments, and learn them jointly with the weights, while using them to dynamically tune the influence of each vertex on its neighbours. We cast learning as a convex optimization problem, and derive an efficient iterative algorithm for solving it. Empirical evaluations on seven NLP data sets demonstrate our algorithm improves over other state-of-the-art graph-based transduction algorithms.