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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 891–900 of 2050 papers

TitleStatusHype
Feedback-Controlled Sequential Lasso Screening—0
Causal Falling Rule Lists—0
Federated Model Search via Reinforcement Learning—0
High SNR Consistent Compressive Sensing—0
A Review of Cross-Sectional Matrix Exponential Spatial Models—0
Aggregation of Affine Estimators—0
Housing Price Prediction Model Selection Based on Lorenz and Concentration Curves: Empirical Evidence from Tehran Housing Market—0
How do some Bayesian Network machine learned graphs compare to causal knowledge?—0
A coupled-mechanisms modelling framework for neurodegeneration—0
Federated Learning with Correlated Data: Taming the Tail for Age-Optimal Industrial IoT—0
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