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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 1371–1380 of 2050 papers

TitleStatusHype
Learning with tree tensor networks: complexity estimates and model selection—0
Learning Word-Level Confidence For Subword End-to-End ASR—0
Least Angle Regression in Tangent Space and LASSO for Generalized Linear Models—0
( β, )-stability for cross-validation and the choice of the number of folds—0
Leveraging free energy in pretraining model selection for improved fine-tuning—0
Leveraging LLMs for MT in Crisis Scenarios: a blueprint for low-resource languages—0
LEVIS: Large Exact Verifiable Input Spaces for Neural Networks—0
Lexical Bias In Essay Level Prediction—0
Lifelong Bayesian Optimization—0
A Geometric Modeling of Occam's Razor in Deep Learning—0
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