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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 471–480 of 2050 papers

TitleStatusHype
Evaluating LLP Methods: Challenges and ApproachesCode0
EPP: interpretable score of model predictive powerCode0
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized LearningCode0
AALF: Almost Always Linear ForecastingCode0
pared: Model selection using multi-objective optimizationCode0
Pareto-optimal clustering with the primal deterministic information bottleneckCode0
Parsimony-Enhanced Sparse Bayesian Learning for Robust Discovery of Partial Differential EquationsCode0
Convex Covariate Clustering for ClassificationCode0
Bayesian Neural Networks at Finite TemperatureCode0
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
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