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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 481–490 of 2050 papers

TitleStatusHype
A novel efficient Multi-view traffic-related object detection framework—0
Corpus-Based Paraphrase Detection Experiments and Review—0
Correcting Model Bias with Sparse Implicit Processes—0
AutoEn: An AutoML method based on ensembles of predefined Machine Learning pipelines for supervised Traffic Forecasting—0
Cost-Effective Online Contextual Model Selection—0
Cost-efficient Knowledge-based Question Answering with Large Language Models—0
A novel framework to quantify uncertainty in peptide-tandem mass spectrum matches with application to nanobody peptide identification—0
Automated discovery of interpretable hyperelastic material models for human brain tissue with EUCLID—0
Deep Clustering using Dirichlet Process Gaussian Mixture and Alpha Jensen-Shannon Divergence Clustering Loss—0
Selective machine learning of doubly robust functionals—0
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