Wavelet-based gradient boosting

Dubossarsky, E; Friedman, JH; Ormerod, JT; Wand, MP

Wavelet-based gradient boosting

Dubossarsky, E Friedman, JH Ormerod, JT Wand, MP

Permalink

Publication Type:: Journal Article
Citation:: Statistics and Computing, 2016, 26 (1-2), pp. 93 - 105
Issue Date:: 2016-01-01

Closed Access

	Filename	Description	Size
	Dubossarsky16.pdf	Published Version	1.11 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Dubossarsky, E	en_US
dc.contributor.author	Friedman, JH	en_US
dc.contributor.author	Ormerod, JT	en_US
dc.contributor.author	Wand, MP https://orcid.org/0000-0003-2555-896X	en_US
dc.date.issued	2016-01-01	en_US
dc.identifier.citation	Statistics and Computing, 2016, 26 (1-2), pp. 93 - 105	en_US
dc.identifier.issn	0960-3174	en_US
dc.identifier.uri	http://hdl.handle.net/10453/41185
dc.description.abstract	© 2014, Springer Science+Business Media New York. A new data science tool named wavelet-based gradient boosting is proposed and tested. The approach is special case of componentwise linear least squares gradient boosting, and involves wavelet functions of the original predictors. Wavelet-based gradient boosting takes advantages of the approximate $$\ell _1$$ℓ1 penalization induced by gradient boosting to give appropriate penalized additive fits. The method is readily implemented in R and produces parsimonious and interpretable regression fits and classifiers.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0877055
dc.relation.ispartof	Statistics and Computing	en_US
dc.relation.isbasedon	10.1007/s11222-014-9474-0	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Wavelet-based gradient boosting	en_US
dc.type	Journal Article
utslib.citation.volume	1-2	en_US
utslib.citation.volume	26	en_US
utslib.for	0104 Statistics	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	closed_access
pubs.issue	1-2	en_US
pubs.publication-status	Published	en_US
pubs.volume	26	en_US

Abstract:

© 2014, Springer Science+Business Media New York. A new data science tool named wavelet-based gradient boosting is proposed and tested. The approach is special case of componentwise linear least squares gradient boosting, and involves wavelet functions of the original predictors. Wavelet-based gradient boosting takes advantages of the approximate $$\ell _1$$ℓ1 penalization induced by gradient boosting to give appropriate penalized additive fits. The method is readily implemented in R and produces parsimonious and interpretable regression fits and classifiers.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/41185