DocumentCode
3116512
Title
Ordinal Least Squares Support Vector Machines - A Discriminant Analysis Approach
Author
Pelckmans, Kristiaan ; Karsmakers, Peter ; Suykens, Johan A K ; De Moor, Bart
Author_Institution
ESAT/SCD-SISTA, Katholieke Univ. Leuven, Leuven
fYear
2006
fDate
6-8 Sept. 2006
Firstpage
247
Lastpage
252
Abstract
This paper explores the extension of the classical ideas behind linear discriminant analysis to the problem of ordinal regression. It is shown how this reasoning fits in a framework of least squares support vector machines (LS-SVMs) and kernel machines, hereby allowing for a nonlinear extension. The resulting method is conceived as a practical alternative to proposed, computationally demanding formulations based on maximal margin.
Keywords
least squares approximations; regression analysis; support vector machines; LS-SVM; kernel machine; least squares support vector machines; linear discriminant analysis; maximal margin; nonlinear extension; ordinal regression; Collaboration; Function approximation; Gaussian processes; Information filtering; Information retrieval; Kernel; Least squares methods; Linear discriminant analysis; Support vector machine classification; Support vector machines;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning for Signal Processing, 2006. Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on
Conference_Location
Arlington, VA
ISSN
1551-2541
Print_ISBN
1-4244-0656-0
Electronic_ISBN
1551-2541
Type
conf
DOI
10.1109/MLSP.2006.275556
Filename
4053655
Link To Document