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Semi-supervised learning based on high density region estimation

✍ Scribed by Hong Chen; Luoqing Li; Jiangtao Peng

Publisher: Elsevier Science
Year: 2010
Tongue: English
Weight: 504 KB
Volume: 23
Category: Article
ISSN: 0893-6080
DOI: 10.1016/j.neunet.2010.06.001

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✦ Synopsis

In this paper, we consider local regression problems on high density regions. We propose a semi-supervised local empirical risk minimization algorithm and bound its generalization error. The theoretical analysis shows that our method can utilize unlabeled data effectively and achieve fast learning rate.

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