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 (View the MathML source) 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, which counts how many other (fictitious) data would be fitted better. LoRP selects the...[Show more]
|Collections||ANU Research Publications|
|Source:||Computational Statistics & Data Analysis|
|Hutter and Tran Model Selection with the Loss Rank Principle 2010.pdf||374.65 kB||Adobe PDF|
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