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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 91–100 of 2050 papers

TitleStatusHype
A Survey and Implementation of Performance Metrics for Self-Organized MapsCode1
A General Model for Aggregating Annotations Across Simple, Complex, and Multi-Object Annotation TasksCode1
Empirical evaluation of scoring functions for Bayesian network model selectionCode1
A network approach to topic modelsCode1
Automatic Model Selection with Large Language Models for ReasoningCode1
Automated Machine Learning in InsuranceCode1
Evaluating Language Models as Synthetic Data GeneratorsCode1
Evaluating natural language processing models with generalization metrics that do not need access to any training or testing dataCode1
Automating Outlier Detection via Meta-LearningCode1
CNN Model & Tuning for Global Road Damage DetectionCode1
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