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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 1751–1760 of 2050 papers

TitleStatusHype
Cost-Sensitive Learning for Predictive Maintenance—0
Coupled differentiation and division of embryonic stem cells inferred from clonal snapshots—0
Cox process representation and inference for stochastic reaction-diffusion processes—0
Cramer-Rao Bound for Estimation After Model Selection and its Application to Sparse Vector Estimation—0
CRIX an index for cryptocurrencies—0
Crossmodal-3600: A Massively Multilingual Multimodal Evaluation Dataset—0
Crossmodal-3600: A Massively Multilingual Multimodal Evaluation Dataset—0
Cross or Wait? Predicting Pedestrian Interaction Outcomes at Unsignalized Crossings—0
Cross Validation Based Model Selection via Generalized Method of Moments—0
Crowd-SFT: Crowdsourcing for LLM Alignment—0
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