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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 421–430 of 2050 papers

TitleStatusHype
Clustering with Fast, Automated and Reproducible assessment applied to longitudinal neural tracking—0
CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure—0
A novel efficient Multi-view traffic-related object detection framework—0
Cognito: Automated Feature Engineering for Supervised Learning—0
A Strong Baseline for Batch Imitation Learning—0
Collab: Controlled Decoding using Mixture of Agents for LLM Alignment—0
Collaborative-controlled LASSO for Constructing Propensity Score-based Estimators in High-Dimensional Data—0
Collaborative Deep Learning for Speech Enhancement: A Run-Time Model Selection Method Using Autoencoders—0
Combinatorially Generated Piecewise Activation Functions—0
A novel framework to quantify uncertainty in peptide-tandem mass spectrum matches with application to nanobody peptide identification—0
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