Title: Clustering unlabeled data with SOMs improves classification of labeled real-world data
Abstract:We show the use of a self organizing map to cluster unlabeled data and to infer possible labelings from the clusters. Our inferred labels are presented to a multilayer perceptron along with labeled da...We show the use of a self organizing map to cluster unlabeled data and to infer possible labelings from the clusters. Our inferred labels are presented to a multilayer perceptron along with labeled data, performance is improved over using only the labeled data. Results are presented for a number of popular real-world benchmark problems from domains other than text. This shows one way in which unlabeled data can be used to enhance supervised learning in a general-purpose neural network.Read More
Publication Year: 2003
Publication Date: 2003-06-25
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 80
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