Referenced Items are not available for this document.
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:
Neural Networks, IEEE Transactions on
(Volume:12
,
Issue:
6
)
Date of Publication: Nov 2001
- Page(s):
- 1525 - 1529
- ISSN :
- 1045-9227
- INSPEC Accession Number:
- 7143368
- Digital Object Identifier :
- 10.1109/72.963791
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 06 August 2002
- Issue Date :
- Nov 2001
- Sponsored by :
- IEEE Computational Intelligence Society
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