An All-at-once Unimodal SVM Approach for Ordinal Classification

• Authors: Joaquim F. Pinto da Costa;Ricardo Sousa;Jaime S. Cardoso
• Venue: ICMLA '10 Proceedings of the 2010 Ninth International Conference on Machine Learning and Applications
• Year: 2010

Support vector machines (SVMs) were initially proposed to solve problems with two classes. Despite the myriad of schemes for multiclassification with SVMs proposed since then, little work has been done for the case where the classes are ordered. Usually one constructs a nominal classifier and a posteriori defines the order. The definition of an ordinal classifier leads to a better generalisation. Moreover, most of the techniques presented so far in the literature can generate ambiguous regions. All-at-Once methods have been proposed to solve this issue. In this work we devise a new SVM methodology based on the unimodal paradigm with the All-at-Once scheme for the ordinal classification.