A new perspective on boosting in linear regression via subgradient optimization and relatives
We analyze boosting algorithms [Ann. Statist. 29 (2001) 1189–1232; Ann. Statist. 28 (2000) 337–407; Ann. Statist. 32 (2004) 407–499] in linear regression from a new perspective: that of modern first-order methods in convex optimiz ation. We show that classic boosting algorithms in linear regression,...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Published: |
Institute of Mathematical Statistics
2018
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| Online Access: | http://hdl.handle.net/1721.1/115300 https://orcid.org/0000-0002-1733-5363 https://orcid.org/0000-0002-5617-1058 |