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Model Selection

Given a set of candidate models, the goal of Model Selection is to select the model that best approximates the observed data and captures its underlying regularities. Model Selection criteria are defined such that they strike a balance between the goodness of fit, and the generalizability or complexity of the models.

Source: Kernel-based Information Criterion

Papers

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Showing 1631–1640 of 2050 papers

TitleStatusHype
Parameter-wise co-clustering for high-dimensional data—0
Multiclass Universum SVMCode0
On an improvement of LASSO by scaling—0
Optimizing the Union of Intersections LASSO (UoI_LASSO) and Vector Autoregressive (UoI_VAR) Algorithms for Improved Statistical Estimation at Scale—0
Use Of Vapnik-Chervonenkis Dimension in Model Selection—0
Robust high dimensional factor models with applications to statistical machine learning—0
An Occam's Razor View on Learning Audiovisual Emotion Recognition with Small Training Sets—0
Model selection by minimum description length: Lower-bound sample sizes for the Fisher information approximation—0
Using J-K-fold Cross Validation To Reduce Variance When Tuning NLP ModelsCode0
Cross Validation Based Model Selection via Generalized Method of Moments—0
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