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AOSO-LogitBoost: Adaptive one-vs-one LogitBoost for multi-class problem

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]

CollectionsANU Research Publications
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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