Multiclass support vector machines using adaptive directed acyclic graph

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.