2002 | OriginalPaper | Buchkapitel
Constraint Classification: A New Approach to Multiclass Classification
verfasst von : Sariel Har-Peled, Dan Roth, Dav Zimak
Erschienen in: Algorithmic Learning Theory
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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In this paper, we present a newviewof 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.