Latent Gaussian Model Boosting

Latent Gaussian models and boosting are widely used techniques in statistics and machine learning. Tree-boosting shows excellent prediction accuracy on many data sets, but potential drawbacks are that it assumes conditional independence of samples, produces discontinuous predictions for, e.g., spati...

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Bibliographic Details

Published in:	IEEE transactions on pattern analysis and machine intelligence Vol. 45; no. 2; pp. 1894 - 1905
Main Author:	Sigrist, Fabio
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.02.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Accuracy Analytical models Boosting Data models Gaussian process gaussian processes Machine learning mixed effects models Predictive models Spatial data Spatial databases Splines (mathematics)
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online Access:	Get full text
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