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- W2035532435 abstract "This paper proposes a new automatic forward model selection method based on leave-one-out (LOO) cross-validation and a fast forward recursive algorithm. It can automatically select a sparse model by incrementally minimizing a LOO test error from a pool of candidate model terms, without the need to specify an additional termination criterion. By defining a proper regression context, the computational efficiency is ensured. A numerical example demonstrates the effectiveness of the proposed method." @default.
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- W2035532435 date "2009-01-01" @default.
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- W2035532435 title "Automatic Forward Model Selection Based on Leave-One-Out Cross-Validation" @default.
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- W2035532435 doi "https://doi.org/10.3182/20090706-3-fr-2004.00145" @default.
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