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Technical Report NC-TR-02-123
2002-123
Bounding the Capacity Measure of Multi-Class Discriminant Models
Yann Guermeur
Andre Elisseeff
Dominique Zelus
ABSTRACT
Vapnik's statistical learning theory has mainly been developed for two
types of problems: pattern recognition (computation of dichotomies) and
regression (estimation of real-valued functions). Multi-class discriminant
analysis has only been studied independently in recent years. Extending
several standard results, among which a famous theorem by Bartlett,
we have derived distribution-free uniform strong laws of large numbers
devoted to multi-class discriminant models. This technical report deals
with the computation of the capacity measures involved in these bounds
on the expected risk. It considers more specifically the case of multi-class
SVMs.
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