Neural networks and the bias/variance dilemma
Neural Computation
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Probabilistic modeling for face orientation discrimination: learning from labeled and unlabeled data
Proceedings of the 1998 conference on Advances in neural information processing systems II
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Neural Computation
Neural Computation
Semi-supervised learning in knowledge discovery
Fuzzy Sets and Systems
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In recent years, there has been growing interest in applying techniques that incorporate knowledge from unlabeled data into systems performing supervised learning. However, disparate results have been presented in the literature, and there is no general consensus that the use of unlabeled examples should always improve classifier performance. This paper proposes a method for incorporating a corpus of unlabeled examples into the supervised training of a neural network classifier and presents results from applying the technique to several datasets from the UCI repository. While the results do not provide support for the claim that unlabeled data can improve overall classification accuracy, a bias-variance decomposition shows that classifiers trained with unlabeled data display lower bias and higher variance than classifiers trained using labeled data alone.