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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 1741–1750 of 2050 papers

TitleStatusHype
Continual Learning Without Knowing Task Identities: Rethinking Occam's Razor—0
Continuous Bayesian Model Selection for Multivariate Causal Discovery—0
Sensitivity to control signals in triphasic rhythmic neural systems: a comparative mechanistic analysis via infinitesimal local timing response curves—0
Convergence Properties of Kronecker Graphical Lasso Algorithms—0
Convex Techniques for Model Selection—0
Boosting with copula-based components—0
Corpus-Based Paraphrase Detection Experiments and Review—0
Correcting Model Bias with Sparse Implicit Processes—0
Cost-Effective Online Contextual Model Selection—0
Cost-efficient Knowledge-based Question Answering with Large Language Models—0
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