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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 1261–1270 of 2050 papers

TitleStatusHype
A Machine Learning Approach to DoA Estimation and Model Order Selection for Antenna Arrays with Subarray Sampling—0
Small Data, Big Decisions: Model Selection in the Small-Data Regime—0
A first econometric analysis of the CRIX family—0
A Meta-learning based Distribution System Load Forecasting Model Selection Framework—0
Quantile Factor Models—0
Non-asymptotic oracle inequalities for the Lasso in high-dimensional mixture of experts—0
Automating Outlier Detection via Meta-LearningCode1
CRIX an index for cryptocurrencies—0
Towards Portfolios of Streamlined Constraint Models: A Case Study with the Balanced Academic Curriculum ProblemCode0
Ridge Regression Revisited: Debiasing, Thresholding and Bootstrap—0
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