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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 681–690 of 2050 papers

TitleStatusHype
A Model Selection Approach for Corruption Robust Reinforcement Learning—0
Corpus-Based Paraphrase Detection Experiments and Review—0
Boosting with copula-based components—0
Convex Techniques for Model Selection—0
AutoAI-TS: AutoAI for Time Series Forecasting—0
A model selection approach for clustering a multinomial sequence with non-negative factorization—0
Adaptive and Calibrated Ensemble Learning with Dependent Tail-free Process—0
Absolute convergence and error thresholds in non-active adaptive sampling—0
Nonlinear Causal Discovery for Grouped Data—0
Convergence Properties of Kronecker Graphical Lasso Algorithms—0
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