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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 1241–1250 of 2050 papers

TitleStatusHype
Greedy metrics in orthogonal greedy learning—0
Greedy Model Averaging—0
Green AI in Action: Strategic Model Selection for Ensembles in Production—0
Green Runner: A tool for efficient deep learning component selection—0
Green Runner: A tool for efficient model selection from model repositories—0
GRIDS: Grouped Multiple-Degradation Restoration with Image Degradation Similarity—0
Group-Sparse Model Selection: Hardness and Relaxations—0
Guided Recommendation for Model Fine-Tuning—0
Guided Sampling-based Evolutionary Deep Neural Network for Intelligent Fault Diagnosis—0
GujiBERT and GujiGPT: Construction of Intelligent Information Processing Foundation Language Models for Ancient Texts—0
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