Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
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Semi-supervised learning for structured output variables
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Deterministic annealing for semi-supervised kernel machines
ICML '06 Proceedings of the 23rd international conference on Machine learning
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
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Proceedings of the 25th international conference on Machine learning
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SS '10 Proceedings of the NAACL HLT 2010 Workshop on Semantic Search
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Semi-Supervised Learning with Measure Propagation
The Journal of Machine Learning Research
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We address two critical issues involved in applying semi-supervised learning (SSL) to a real-world task: parameter tuning and choosing which (if any) SSL algorithm is best suited for the task at hand. To gain a better understanding of these issues, we carry out a medium-scale empirical study comparing supervised learning (SL) to two popular SSL algorithms on eight natural language processing tasks under three performance metrics. We simulate how a practitioner would go about tackling a new problem, including parameter tuning using cross validation (CV). We show that, under such realistic conditions, each of the SSL algorithms can be worse than SL on some datasets. However, we also show that CV can select SL/SSL to achieve "agnostic SSL," whose performance is almost always no worse than SL. While CV is often dismissed as unreliable for SSL due to the small amount of labeled data, we show that it is in fact effective for accuracy even when the labeled dataset size is as small as 10.