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An improved multiclass LogitBoost using Adaptive-One-Vs-One

Sun, Peng; Reid, Mark; Zhou, Jie


LogitBoost is a popular Boosting variant that can be applied to either binary or multi-class classification. From a statistical viewpoint LogitBoost can be seen as additive tree regression by minimizing the Logistic loss. Following this setting, it is still non-trivial to devise a sound multi-class LogitBoost compared with to devise its binary counterpart. The difficulties are due to two important factors arising in multiclass Logistic loss. The first is the invariant property implied by the...[Show more]

CollectionsANU Research Publications
Date published: 2014
Type: Journal article
Source: Machine Learning
DOI: 10.1007/s10994-014-5434-3


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