AUC-Maximizing Ensembles through Metalearning. - Wikidata - wikimedia - TriplyDB

AUC-Maximizing Ensembles through Metalearning.

AUC-Maximizing Ensembles through Metalearning.

http://www.wikidata.org/entity/Q53807765

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Constrained binary classification using ensemble learning: an application to cost-efficient targeted PrEP strategies.Predicting posttraumatic stress disorder following a natural disaster.

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AUC-Maximizing Ensembles through Metalearning.

http://www.wikidata.org/entity/Q53807765

description

2016 nî lūn-bûn

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2016年の論文

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2016年学术文章

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2016年学术文章

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2016年学术文章

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2016年学术文章

@zh-my

2016年学术文章

@zh-sg

2016年學術文章

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2016年學術文章

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2016年學術文章

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name

AUC-Maximizing Ensembles through Metalearning.

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AUC-Maximizing Ensembles through Metalearning.

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type

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AUC-Maximizing Ensembles through Metalearning.

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AUC-Maximizing Ensembles through Metalearning.

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prefLabel

AUC-Maximizing Ensembles through Metalearning.

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AUC-Maximizing Ensembles through Metalearning.

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International Journal of Biostatistics

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AUC-Maximizing Ensembles through Metalearning.

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Erin LeDell

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Mark J van der Laan

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Maya Petersen

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P2860

Analyzing a portion of the ROC curve.

Impact of HbA1c measurement on hospital readmission rates: analysis of 70,000 clinical database patient records.

Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates

Searching for exotic particles in high-energy physics with deep learning.

A receiver operating characteristic partial area index for highly sensitive diagnostic tests.

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203-218

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scholarly article

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10.1515/IJB-2015-0035

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1

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12

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2016-05-01T00:00:00Z

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27227721

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4912128

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