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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 441–450 of 2050 papers

TitleStatusHype
A novel framework to quantify uncertainty in peptide-tandem mass spectrum matches with application to nanobody peptide identification—0
Comparative Study of Machine Learning Algorithms in Detecting Cardiovascular Diseases—0
Comparing Bayesian Models of Annotation—0
Comparison between Suitable Priors for Additive Bayesian Networks—0
Asymmetrically Weighted CCA And Hierarchical Kernel Sentence Embedding For Image & Text Retrieval—0
Comparison of Bayesian predictive methods for model selection—0
Cost-Effective Online Contextual Model Selection—0
Selective machine learning of doubly robust functionals—0
Complex decision-making strategies in a stock market experiment explained as the combination of few simple strategies—0
Local Projections Inference with High-Dimensional Covariates without Sparsity—0
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