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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 1511–1520 of 2050 papers

TitleStatusHype
An Innovative Next Activity Prediction Approach Using Process Entropy and DAW-Transformer—0
An Instrumental Variables Approach to Testing Firm Conduct—0
An Introductory Guide to Fano's Inequality with Applications in Statistical Estimation—0
An Investigation into Seasonal Variations in Energy Forecasting for Student Residences—0
Unsupervised Optimisation of GNNs for Node Clustering—0
Non-asymptotic oracle inequalities for the Lasso in high-dimensional mixture of experts—0
An Occam's Razor View on Learning Audiovisual Emotion Recognition with Small Training Sets—0
Anomaly Detection for an E-commerce Pricing System—0
Non-asymptotic model selection in block-diagonal mixture of polynomial experts models—0
A Nonparametric Bayesian Approach to Uncovering Rat Hippocampal Population Codes During Spatial Navigation—0
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