Constraint Classification: A New Approach To Multiclass Classification
ALT '02: Proceedings of the 13th International Conference on Algorithmic Learning Theory(2002)
摘要
In this paper, we present a new view of multiclass classification and introduce the constraint classification problem, a generalization that captures many flavors of multiclass classification. We provide the first optimal, distribution independent bounds for many multiclass learning algorithms, including winner-take-all (WTA). Based on our view, we present a learning algorithm that learns via a single,linear classifier in high dimension. In addition to the distribution independent bounds, we provide a simple margin-based analysis improving generalization bounds for linear multiclass support vector machines.
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关键词
Partial Order, Growth Function, Output Space, Multiclass Classification, Hypothesis Class
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