AOSO-LogitBoost: Adaptive one-vs-one LogitBoost for multi-class problem
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Sun, Peng; Reid, Mark; Zhou, Jie
Description
This paper presents an improvement to model learning when using multi-class LogitBoost for classification. Motivated by the statistical view, LogitBoost can be seen as additive tree regression. Two important factors in this setting are: 1) coupled classifier output due to a sum-to-zero constraint, and 2) the dense Hessian matrices that arise when computing tree node split gain and node value fittings. In general, this setting is too complicated for a tractable model learning algorithm. However,...[Show more]
Collections | ANU Research Publications |
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Date published: | 2012 |
Type: | Conference paper |
URI: | http://hdl.handle.net/1885/68758 |
Source: | Proceedings of the 29th International Conference on Machine Learning, ICML 2012 |
DOI: | 10.3907 |
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File | Description | Size | Format | Image |
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01_Sun_AOSO-LogitBoost:_Adaptive_2012.pdf | 317.87 kB | Adobe PDF | Request a copy |
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