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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 1231–1240 of 2050 papers

TitleStatusHype
GPT in Data Science: A Practical Exploration of Model Selection—0
Gradient-based Hyperparameter Optimization without Validation Data for Learning fom Limited Labels—0
Gradient Boosting for Spatial Regression Models with Autoregressive Disturbances—0
Graph-based regularization for regression problems with alignment and highly-correlated designs—0
Graph Coding for Model Selection and Anomaly Detection in Gaussian Graphical Models—0
Graphical LASSO Based Model Selection for Time Series—0
Graph Similarity Description: How Are These Graphs Similar?—0
Graph vs. Sequence: An Empirical Study on Knowledge Forms for Knowledge-Grounded Dialogue—0
GRASMOS: Graph Signage Model Selection for Gene Regulatory Networks—0
Greedy equivalence search for nonparametric graphical models—0
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