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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 1381–1390 of 2050 papers

TitleStatusHype
Likelihood Adaptively Modified Penalties—0
Limits of Model Selection under Transfer Learning—0
LIMSI@CoNLL'17: UD Shared Task—0
Linear Bandits with Memory: from Rotting to Rising—0
Linearised Laplace Inference in Networks with Normalisation Layers and the Neural g-Prior—0
LLM4DS: Evaluating Large Language Models for Data Science Code Generation—0
LLMProxy: Reducing Cost to Access Large Language Models—0
LM-BIC Model Selection in Semiparametric Models—0
Logistic principal component analysis via non-convex singular value thresholding—0
Logits-Constrained Framework with RoBERTa for Ancient Chinese NER—0
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