DocumentCode
1648931
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
Volume
1
fYear
2002
fDate
6/24/1905 12:00:00 AM
Firstpage
980
Lastpage
985
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location
Honolulu, HI
ISSN
1098-7576
Print_ISBN
0-7803-7278-6
Type
conf
DOI
10.1109/IJCNN.2002.1005608
Filename
1005608
Link To Document