DocumentCode
423564
Title
A new method for multiclass support vector machines
Author
Anguita, Davide ; Ridella, Sandro ; Sterpi, Dario
Author_Institution
Dept. of Biophys. & Electron. Eng., Genoa Univ., Italy
Volume
1
fYear
2004
fDate
25-29 July 2004
Lastpage
412
Abstract
In this paper we present a new method for solving multiclass problems with a support vector machine. Our method compares favorably with other proposals, appeared so far in the literature, both in terms of computational needs for the feedforward phase and of classification accuracy. The main result, however, is the mapping of the multiclass problem to a biclass one, which allows us to suggest a method for estimating the generalization error by using data-dependent error bounds.
Keywords
feedforward; generalisation (artificial intelligence); pattern classification; support vector machines; classification accuracy; data-dependent error bounds; feedforward phase; generalization error; multiclass support vector machines; Machine learning; Machine learning algorithms; Proposals; Support vector machine classification; Support vector machines;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on
ISSN
1098-7576
Print_ISBN
0-7803-8359-1
Type
conf
DOI
10.1109/IJCNN.2004.1379940
Filename
1379940
Link To Document