Difference between revisions of "Publications:Using Labelled and Unlabelled Data to Train a Multilayer Perceptron for Colour Classification in Graphic Arts"
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| − | |Name=Verikas, Antanas | + | |Name=Verikas, Antanas (av) (0000-0003-2185-8973) (Högskolan i Halmstad (2804), Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) (3905), Halmstad Embedded and Intelligent Systems Research (EIS) (3938));Gelzinis, Adas (Kaunas University of Technology, Kaunas, Lithuania);Malmqvist, Kerstin (kermalx) (Högskolan i Halmstad (2804), Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) (3905), Halmstad Embedded and Intelligent Systems Research (EIS) (3938)) |
|Title=Using Labelled and Unlabelled Data to Train a Multilayer Perceptron for Colour Classification in Graphic Arts | |Title=Using Labelled and Unlabelled Data to Train a Multilayer Perceptron for Colour Classification in Graphic Arts | ||
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|PublicationDate=2010-02-25 | |PublicationDate=2010-02-25 | ||
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|diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:300237}} | |diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:300237}} | ||
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| Title | Using Labelled and Unlabelled Data to Train a Multilayer Perceptron for Colour Classification in Graphic Arts |
|---|---|
| Author | |
| Year | 1999 |
| PublicationType | Conference Paper |
| Journal | |
| HostPublication | Multiple approaches to intelligent systems : 12th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems IEA/AIE-99, Cairo, Egypt, May 31 - June 3, 1999. Proceedings |
| Conference | 12th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems IEA/AIE -99, Cairo, Egypt, May 31 - June 3, 1999 |
| DOI | http://dx.doi.org/10.1007/978-3-540-48765-4_59 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:300237 |
| Abstract | This paper presents an approach to using both labelled and unlabelled data to train a multi-layer 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 networks for colour classification in graphic arts. |
