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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 1181–1190 of 2050 papers

TitleStatusHype
Derivative-Free Reinforcement Learning: A Review—0
Ising Model Selection Using _1-Regularized Linear Regression: A Statistical Mechanics Analysis—0
Zero Training Overhead Portfolios for Learning to Solve Combinatorial Problems—0
Vine copula mixture models and clustering for non-Gaussian dataCode0
Graph Coding for Model Selection and Anomaly Detection in Gaussian Graphical Models—0
Ranking vs. Classifying: Measuring Knowledge Base Completion QualityCode0
Bayesian data-driven discovery of partial differential equations with variable coefficients—0
A linearized framework and a new benchmark for model selection for fine-tuning—0
Sequential prediction under log-loss and misspecification—0
Choice modelling in the age of machine learning - discussion paper—0
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