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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
Collaborative Deep Learning for Speech Enhancement: A Run-Time Model Selection Method Using Autoencoders—0
Consistencies and inconsistencies between model selection and link prediction in networks—0
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction—0
( β, )-stability for cross-validation and the choice of the number of folds—0
Beyond similarity assessment: Selecting the optimal model for sequence alignment via the Factorized Asymptotic Bayesian algorithm—0
Spectral-graph Based Classifications: Linear Regression for Classification and Normalized Radial Basis Function Network—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
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