Petrakieva, L. and Gabrys, B., 2002. Selective Sampling for Combined Learning from Labelled and Unlabelled Data. In: 4th International Conference on Recent Advances in Soft Computing RASC 2002: Proceedings, December 2002, Nottingham, England. (Unpublished)
Full text not available from this repository.
Official URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=1...
This paper examines the problem of selecting a suitable subset of data to be labelled when building pattern classifiers from labelled and unlabelled data. The selection of representative set is guided by a clustering information and various options of allocating a number of samples within clusters and their distributions are investigated. The experimental results show that hybrid methods like Semi-supervised clustering with selective sampling can result in building a classifier which requires much less labelled data in order to achieve a comparable classification performance to classifiers built only on the basis of labelled data.
|Item Type:||Conference or Workshop Item (Paper)|
|Subjects:||Generalities > Computer Science and Informatics|
|Group:||School of Design, Engineering & Computing > Smart Technology Research Centre|
|Deposited By:||INVALID USER|
|Deposited On:||20 Dec 2008 18:38|
|Last Modified:||07 Mar 2013 15:02|
|Repository Staff Only -|
|BU Staff Only -|
|Help Guide -||Editing Your Items in BURO|