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

• Type: Article in International Conference Proceedings Book
• Date: 2010

Authors

Pinto Da Costa, JF (Author) FCUP; Sousa, R (Author) Other; Cardoso, JS (Author) FEUP

Conference proceedings International

Title: Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010

Pages: 59-64

9th International Conference on Machine Learning and Applications, ICMLA 2010

Washington, DC, 12 December 2010 through 14 December 2010

Indexing

ISI Web of Knowledge

Scopus - 15 Citations

Other information

Authenticus ID: P-007-XP6

DOI: 10.1109/icmla.2010.16

Abstract (EN): 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. © 2010 IEEE.

Language: English

Type (Professor's evaluation): Scientific