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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 801–810 of 2050 papers

TitleStatusHype
Forecasting large collections of time series: feature-based methods—0
Forecasting Whole-Brain Neuronal Activity from Volumetric Video—0
Determination of Latent Dimensionality in International Trade Flow—0
Forward and Backward Feature Selection for Query Performance Prediction—0
Forward utilities and Mean-field games under relative performance concerns—0
Detection of Unobserved Common Causes based on NML Code in Discrete, Mixed, and Continuous Variables—0
A Bayesian Model for Bivariate Causal Inference—0
Foundation of Calculating Normalized Maximum Likelihood for Continuous Probability Models—0
Frame Fusion with Vehicle Motion Prediction for 3D Object Detection—0
Accessible, At-Home Detection of Parkinson's Disease via Multi-task Video Analysis—0
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