Model selection with the Loss Rank Principle
نویسندگان
چکیده
منابع مشابه
Model Selection with the Loss Rank Principle
A key issue in statistics and machine learning is to automatically select the “right” model complexity, e.g., the number of neighbors to be averaged over in k nearest neighbor (kNN) regression or the polynomial degree in regression with polynomials. We suggest a novel principle the Loss Rank Principle (LoRP) for model selection in regression and classification. It is based on the loss rank, whi...
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We introduce a new principle for model selection in regression and classification. Many regression models are controlled by some smoothness or flexibility or complexity parameter c, e.g. the number of neighbors to be averaged over in k nearest neighbor (kNN) regression or the polynomial degree in regression with polynomials. Let f̂ c D be the (best) regressor of complexity c on data D. A more fl...
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Hutter (2007) recently introduced the loss rank principle (LoRP) as a generalpurpose principle for model selection. The LoRP enjoys many attractive properties and deserves further investigations. The LoRP has been well-studied for regression framework in Hutter and Tran (2010). In this paper, we study the LoRP for classification framework, and develop it further for model selection problems in ...
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ژورنال
عنوان ژورنال: Computational Statistics & Data Analysis
سال: 2010
ISSN: 0167-9473
DOI: 10.1016/j.csda.2009.11.015