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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 1291–1300 of 2050 papers

TitleStatusHype
Improving Robustness and Uncertainty Modelling in Neural Ordinary Differential Equations—0
Improving VTE Identification through Adaptive NLP Model Selection and Clinical Expert Rule-based Classifier from Radiology Reports—0
Inconsistency of cross-validation for structure learning in Gaussian graphical models—0
Incremental Learning for Fully Unsupervised Word Segmentation Using Penalized Likelihood and Model Selection—0
Independent Mobility GPT (IDM-GPT): A Self-Supervised Multi-Agent Large Language Model Framework for Customized Traffic Mobility Analysis Using Machine Learning Models—0
Individual Text Corpora Predict Openness, Interests, Knowledge and Level of Education—0
In-Domain African Languages Translation Using LLMs and Multi-armed Bandits—0
Inertial Regularization and Selection (IRS): Sequential Regression in High-Dimension and Sparsity—0
InfantCryNet: A Data-driven Framework for Intelligent Analysis of Infant Cries—0
Inferring bias and uncertainty in camera calibration—0
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