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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 1451–1475 of 2050 papers

TitleStatusHype
Modeling Transient Changes in Circadian Rhythms—0
Modelling brain lesion volume in patches with CNN-based Poisson Regression—0
Modelling tourism demand to Spain with machine learning techniques. The impact of forecast horizon on model selection—0
Model Order Selection Based on Information Theoretic Criteria: Design of the Penalty—0
Model-Parallel Model Selection for Deep Learning Systems—0
Model Selection, Adaptation, and Combination for Transfer Learning in Wind and Photovoltaic Power Forecasts—0
Model Selection and Overfitting in Genetic Programming: Empirical Study [Extended Version]—0
Model selection and velocity estimation using novel priors for motion patterns—0
Model selection by minimum description length: Lower-bound sample sizes for the Fisher information approximation—0
Model Selection CNN-based VVC QualityEnhancement—0
Model selection consistency from the perspective of generalization ability and VC theory with an application to Lasso—0
Model Selection for Anomaly Detection—0
Model Selection for Average Reward RL with Application to Utility Maximization in Repeated Games—0
Model selection for behavioral learning data and applications to contextual bandits—0
Model Selection for Cross-Lingual Transfer using a Learned Scoring Function—0
Model selection for deep audio source separation via clustering analysis—0
Model Selection for Degree-corrected Block Models—0
Model Selection for Gaussian-gated Gaussian Mixture of Experts Using Dendrograms of Mixing Measures—0
Model Selection for Gaussian Process Regression by Approximation Set Coding—0
Model Selection for Generalized Zero-shot Learning—0
Model Selection for Generic Contextual Bandits—0
Model Selection for High-Dimensional Regression under the Generalized Irrepresentability Condition—0
Model selection for high-dimensional linear regression with dependent observations—0
Model Selection for Nonnegative Matrix Factorization by Support Union Recovery—0
Model Selection for Off-policy Evaluation: New Algorithms and Experimental Protocol—0
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