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9

PRL
2008

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Fuzzy relevance vector machine for learning from unbalanced data and noise

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Fuzzy relevance vector machine for learning from unbalanced data and noise

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Handing unbalanced data and noise are two important issues in the field of machine learning. This paper proposed a complete framework of fuzzy relevance vector machine by weighting the punishment terms of error in Bayesian inference process of relevance vector machine (RVM). Above problems can be learned within this framework with different kinds of fuzzy membership functions. Experiments on both synthetic data and real world data demonstrate that fuzzy relevance vector machine (FRVM) is effective in dealing with unbalanced data and reducing the effects of noises or outliers.

Dingfang Li, Wenchao Hu, Wei Xiong, Jin-Bo Yang

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Added	14 Dec 2010
Updated	14 Dec 2010
Type	Journal
Year	2008
Where	PRL
Authors	Dingfang Li, Wenchao Hu, Wei Xiong, Jin-Bo Yang

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