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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 1761–1770 of 2050 papers

TitleStatusHype
Spectral-graph Based Classifications: Linear Regression for Classification and Normalized Radial Basis Function Network—0
Beyond similarity assessment: Selecting the optimal model for sequence alignment via the Factorized Asymptotic Bayesian algorithm—0
Probabilistic Matrix Factorization for Automated Machine LearningCode0
Automatic Response Category Combination in Multinomial Logistic Regression—0
Inverse Moment Methods for Sufficient Forecasting using High-Dimensional Predictors—0
Hybrid safe-strong rules for efficient optimization in lasso-type problemsCode0
Identifying spatiotemporal dynamics of Ebola in Sierra Leone using virus genomes—0
Dynamic Model Selection for Prediction Under a Budget—0
Boosting with Structural Sparsity: A Differential Inclusion Approach—0
On Bayesian Exponentially Embedded Family for Model Order Selection—0
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