Abstract
This paper 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 represent adequately 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.
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© 2001 Springer-Verlag Berlin Heidelberg
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Verikas, A., Gelzinis, A., Malmqvist, K., Bacauskiene, M. (2001). Using Unlabelled Data to Train a Multilayer Perceptron. In: Singh, S., Murshed, N., Kropatsch, W. (eds) Advances in Pattern Recognition — ICAPR 2001. ICAPR 2001. Lecture Notes in Computer Science, vol 2013. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44732-6_5
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DOI: https://doi.org/10.1007/3-540-44732-6_5
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