Response to Terrorism - The Use of Force Against International Terrorism No 3-14

Abstract

Support vector machine initially developed to perform binary classification. This paper presents a multi-class support
vector machine classifier and ordinal regression to classify the type of bone mineral density. This paper compares the
performance of four multi-class approaches, one-against-all, one-against-one, Weston and Watkins, and Crammer and
Singer. Results from our real life data conclude that Crammer and Singer may be better approach depending on training
error and the percentage of correctly classified test data. Also, we fined that the training error become more less when
the regulization parameter C and kernel parameter O become large.