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Erschienen in:
2007 | OriginalPaper | Buchkapitel
Generalization Error of Automatic Relevance Determination
verfasst von : Shinichi Nakajima, Sumio Watanabe
Erschienen in: Artificial Neural Networks – ICANN 2007
Verlag: Springer Berlin Heidelberg
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The automatic relevance determination (ARD) shows good performance in many applications. Recently, it has been applied to brain current estimation with the variational method. Although people who use the ARD tend to pay attention to one benefit of the ARD, sparsity, we, in this paper, focus on another benefit, generalization. In this paper, we clarify the generalization error of the ARD in the case that a class of prior distributions is used, and show that good generalization is caused by singularities of the ARD. Sparsity is not observed in that case, however, the mechanism that the singularities provide good generalization implies the mechanism that they also provide sparsity.