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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 1401–1410 of 2050 papers

TitleStatusHype
Machine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials -- A minireview—0
Debiased Machine Learning of Set-Identified Linear Models—0
Machine-learning Growth at Risk—0
Machine learning with incomplete datasets using multi-objective optimization models—0
Mallows-type model averaging: Non-asymptotic analysis and all-subset combination—0
Mantis: Enabling Energy-Efficient Autonomous Mobile Agents with Spiking Neural Networks—0
Marginal likelihood and model selection for Gaussian latent tree and forest models—0
Marginal likelihood computation for model selection and hypothesis testing: an extensive review—0
Marked point processes and intensity ratios for limit order book modeling—0
Markov models for ocular fixation locations in the presence and absence of colour—0
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