Title :
Multiclass support vector machines using adaptive directed acyclic graph
Author :
Kijsirikul, Boonserm ; Ussivakul, Nitiwut
Author_Institution :
Dept. of Comput. Eng., Chulalongkorn Univ., Bangkok, Thailand
fDate :
6/24/1905 12:00:00 AM
Abstract :
Presents a method of extending support vector machines (SVMs) for dealing with multiclass problems. Motivated by the decision directed acyclic graph (DDAG), we propose the adaptive DAG (ADAG): a modified structure of the DDAG that has a lower number of decision levels and reduces the dependency on the sequence of nodes. Thus, the ADAG improves the accuracy of the DDAG while maintaining low computational requirement
Keywords :
directed graphs; learning (artificial intelligence); learning automata; pattern classification; probability; adaptive directed acyclic graph; decision directed acyclic graph; decision levels; linear support vector machines; multiclass support vector machines; Algorithm design and analysis; Speech recognition; Support vector machine classification; Support vector machines; Training data;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1005608
