Selective Sampling for Combined Learning from Labelled and Unlabelled Data
نویسندگان
چکیده
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.
منابع مشابه
Combining labelled and unlabelled data in the design of pattern classification systems
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