Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
The nature of statistical learning theory
The nature of statistical learning theory
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
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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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. The main motivation for this is the belief that classification performance can be improved by utilizing the ccntextual information provided by unlabeled data. Most of the approaches that have been proposed for this problem are generative; that is, they are based on directly modelling the class distribution of the training examples, This paper approaches the problem from a discriminant classifier perspective, and proposes a new technique by which feedforward neural networks can be trained from a dataset consisting of both labeled and unlabeled data. Results are presented from applying the technique to several datasets from the UCI repository. The results show that on some datasets the use of unlabeled examples can lead to an improvement in classification performance over that of conventional supervised learning. The technique adds little computational overhead to the standard supervised technique.