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Sparse Bayesian learning (SBL) and relevance vector machines(RVM) have received much attention in the machine learning, which as a means of achieving regression. The methodology relies on a parameterized prior that encourages models with few non-zero weights. In this paper, we present a new and efficient algorithm which exploits properties of the marginal likelihood function to enable maximisation via a principled and efficient sequential addition and deletion of candidate basis functions. Meanwhile, regression model has been built based on this algorithm.
Artificial Intelligence and Computational Intelligence (AICI), 2010 International Conference on (Volume:1 )
Date of Conference: 23-24 Oct. 2010