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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 271–280 of 2050 papers

TitleStatusHype
Face Spoofing Detection using Deep LearningCode0
Evaluation of dynamic causal modelling and Bayesian model selection using simulations of networks of spiking neuronsCode0
Evaluating LLP Methods: Challenges and ApproachesCode0
Evaluating Large Language Models as Generative User Simulators for Conversational RecommendationCode0
Evaluation of HTR models without Ground Truth MaterialCode0
Estimating Individual Treatment Effects using Non-Parametric Regression Models: a ReviewCode0
A Bayesian Modelling Framework with Model Comparison for Epidemics with Super-SpreadingCode0
Factored Latent-Dynamic Conditional Random Fields for Single and Multi-label Sequence ModelingCode0
A Human-in-the-Loop Fairness-Aware Model Selection Framework for Complex Fairness Objective LandscapesCode0
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
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