Puncturing Multi-class Support Vector Machines View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2002-08-21

AUTHORS

Fernando Pérez-Cruz , Antonio Artés-Rodríguez

ABSTRACT

Non-binary classification has been usually addressed by training several binary classification when using Support Vector Machines (SVMs), because its performance does not degrade compared to the multi-class SVM and it is simpler to train and implement. In this paper we show that the binary classifiers in which the multi-classification relies are not independent from each other and using a puncturing mechanism this dependence can be pruned, obtaining much better multi-classification schemes as shown by the carried out experiments. More... »

PAGES

751-756

Book

TITLE

Artificial Neural Networks — ICANN 2002

ISBN

978-3-540-44074-1
978-3-540-46084-8

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/3-540-46084-5_122

DOI

http://dx.doi.org/10.1007/3-540-46084-5_122

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1032782008


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