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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 1661–1670 of 2050 papers

TitleStatusHype
Boxer: Interactive Comparison of Classifier Results—0
Breaking the bonds of weak coupling: the dynamic causal modelling of oscillator amplitudes—0
Bridge the Modality and Capability Gaps in Vision-Language Model Selection—0
Bridging AIC and BIC: a new criterion for autoregression—0
Bridging factor and sparse models—0
Bridging Information Criteria and Parameter Shrinkage for Model Selection—0
Bridging the Bosphorus: Advancing Turkish Large Language Models through Strategies for Low-Resource Language Adaptation and Benchmarking—0
Budgeted Online Model Selection and Fine-Tuning via Federated Learning—0
Building Chatbots from Forum Data: Model Selection Using Question Answering Metrics—0
Building Robust Machine Learning Models for Small Chemical Science Data: The Case of Shear Viscosity—0
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