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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 361–370 of 2050 papers

TitleStatusHype
Block-diagonal covariance selection for high-dimensional Gaussian graphical models—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
Closing the gap between open-source and commercial large language models for medical evidence summarization—0
Clustering Discrete-Valued Time Series—0
Budgeted Online Model Selection and Fine-Tuning via Federated Learning—0
Cognito: Automated Feature Engineering for Supervised Learning—0
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