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Model selection

Known as: Model comparison 
Model selection is the task of selecting a statistical model from a set of candidate models, given data. In the simplest cases, a pre-existing set of… Expand
Wikipedia

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2020
Review
2020
The new demands for high-reliability and ultra-high capacity wireless communication have led to extensive research into 5G… Expand
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Review
2019
Review
2019
With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications… Expand
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Review
2018
Review
2018
Abstract This study presents an evaluation of the Community of Inquiry (CoI) survey instrument developed by Arbaugh et al. (2008… Expand
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Review
2018
Review
2018
The correct use of model evaluation, model selection, and algorithm selection techniques is vital in academic machine learning… Expand
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Highly Cited
2006
Highly Cited
2006
Sparsity or parsimony of statistical models is crucial for their proper interpretations, as in sciences and social sciences… Expand
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Highly Cited
2001
Highly Cited
2001
Given a data set, you can fit thousands of models at the push of a button, but how do you choose the best? With so many candidate… Expand
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Highly Cited
1998
Highly Cited
1998
The first € price and the £ and $ price are net prices, subject to local VAT. Prices indicated with * include VAT for books; the… Expand
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Highly Cited
1998
Highly Cited
1998
Introduction to model selection the univariate regression model the univariate autoregressive model the multivariate regression… Expand
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Highly Cited
1989
Highly Cited
1989
SUMMARY A bias correction to the Akaike information criterion, AIC, is derived for regression and autoregressive time series… Expand
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Highly Cited
1989
Highly Cited
1989
Using the Kullback-Leibler information criterion to measure the closeness of a model to the truth, the author proposes new… Expand
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