In:
Advanced Materials Research, Trans Tech Publications, Ltd., Vol. 926-930 ( 2014-5), p. 3008-3011
Abstract:
Currently, most researchers select clustering-based algorithms to generate the initial training set for active learning. Considering that for such algorithms, a single clustering is not stable, we propose an initial training set selection algorithm which combines multi-clustering results to select samples. Specifically, after each clustering, it delimits several representative regions. If a sample falls into its corresponding representative region, then the algorithm casts a vote for it to mark that it is a potential representative sample. Finally, after several clustering, the samples with the most votes are selected. Experimental results show that our algorithm can efficiently select the informative samples, and can make the classifier have a more stable performance.
Type of Medium:
Online Resource
ISSN:
1662-8985
DOI:
10.4028/www.scientific.net/AMR.926-930
DOI:
10.4028/www.scientific.net/AMR.926-930.3008
Language:
Unknown
Publisher:
Trans Tech Publications, Ltd.
Publication Date:
2014
detail.hit.zdb_id:
2265002-7
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