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
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;
Conference_Titel :
Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on
Print_ISBN :
0-7803-8359-1
DOI :
10.1109/IJCNN.2004.1379940
