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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 1811–1820 of 2050 papers

TitleStatusHype
Model Selection for Gaussian Process Regression by Approximation Set Coding—0
Searching parsimonious solutions with GA-PARSIMONY and XGboost in high-dimensional databases—0
Universum Learning for Multiclass SVM—0
Robust Regression For Image Binarization Under Heavy Noises and Nonuniform Background—0
Learning conditional independence structure for high-dimensional uncorrelated vector processes—0
GTApprox: surrogate modeling for industrial designCode0
Feedback-Controlled Sequential Lasso Screening—0
Large-scale Collaborative Imaging Genetics Studies of Risk Genetic Factors for Alzheimer's Disease Across Multiple Institutions—0
Bayesian Model Selection Methods for Mutual and Symmetric k-Nearest Neighbor Classification—0
Learning Dynamic Hierarchical Models for Anytime Scene Labeling—0
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