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Empirical measure of multiclass generalization performance: the K-winner machine case

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2 Author(s)
S. Ridella ; Dept. of Biophys. & Electron. Eng., Genoa Univ., Italy ; R. Zunino

Combining the K-winner machine (KWM) model with empirical measurements of a classifier's Vapnik-Chervonenkis (VC)-dimension gives two major results. First, analytical derivations refine the theory that characterizes the generalization performances of binary classifiers. Second, a straightforward extension of the theoretical framework yields bounds to the generalization error for multiclass problems

Published in:

IEEE Transactions on Neural Networks  (Volume:12 ,  Issue: 6 )