Neural Computation
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
On the exponential value of labeled samples
Pattern Recognition Letters
Face recognition using a hybrid supervised/unsupervised neural network
Pattern Recognition Letters
Colour image segmentation by modular neural network
Pattern Recognition Letters
Time-series segmentation using predictive modular neural networks
Neural Computation
Backpropagation learning algorithms for classification with fuzzy mean square error
Pattern Recognition Letters
Incorporating test inputs into learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
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
IEA/AIE '99 Proceedings of the 12th international conference on Industrial and engineering applications of artificial intelligence and expert systems: multiple approaches to intelligent systems
Training Algorithm with Incomplete Data for Feed-ForwardNeural Networks
Neural Processing Letters
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Partially supervised clustering for image segmentation
Pattern Recognition
Intelligent vocal cord image analysis for categorizing laryngeal diseases
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Semi-supervised learning with multilayer perceptron for detecting changes of remote sensing images
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Change Detection of Remote Sensing Images with Semi-supervised Multilayer Perceptron
Fundamenta Informaticae
Pattern classification and clustering: A review of partially supervised learning approaches
Pattern Recognition Letters
Semi-supervised change detection using modified self-organizing feature map neural network
Applied Soft Computing
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This Letter presents an approach to using both labelled and unlabelled data to train a multilayer perceptron. The unlabelled data are iteratively pre-processed by a perceptron being trained to obtain the soft class label estimates. It is demonstrated that substantial gains in classification performance may be achieved from the use of the approach when the labelled data do not adequately represent the entire class distributions. The experimental investigations performed have shown that the approach proposed may be successfully used to train neural networks for learning different classification problems.