DocumentCode :
23832
Title :
Incremental Support Vector Learning for Ordinal Regression
Author :
Bin Gu ; Sheng, Victor S. ; Keng Yeow Tay ; Romano, Walter ; Shuo Li
Author_Institution :
Jiangsu Eng. Center of Network Monitoring, Nanjing Univ. of Inf. Sci. & Technol., Nanjing, China
Volume :
26
Issue :
7
fYear :
2015
fDate :
Jul-15
Firstpage :
1403
Lastpage :
1416
Abstract :
Support vector ordinal regression (SVOR) is a popular method to tackle ordinal regression problems. However, until now there were no effective algorithms proposed to address incremental SVOR learning due to the complicated formulations of SVOR. Recently, an interesting accurate on-line algorithm was proposed for training ν-support vector classification (ν-SVC), which can handle a quadratic formulation with a pair of equality constraints. In this paper, we first present a modified SVOR formulation based on a sum-of-margins strategy. The formulation has multiple constraints, and each constraint includes a mixture of an equality and an inequality. Then, we extend the accurate on-line ν-SVC algorithm to the modified formulation, and propose an effective incremental SVOR algorithm. The algorithm can handle a quadratic formulation with multiple constraints, where each constraint is constituted of an equality and an inequality. More importantly, it tackles the conflicts between the equality and inequality constraints. We also provide the finite convergence analysis for the algorithm. Numerical experiments on the several benchmark and real-world data sets show that the incremental algorithm can converge to the optimal solution in a finite number of steps, and is faster than the existing batch and incremental SVOR algorithms. Meanwhile, the modified formulation has better accuracy than the existing incremental SVOR algorithm, and is as accurate as the sum-of-margins based formulation of Shashua and Levin.
Keywords :
convergence; learning (artificial intelligence); pattern classification; regression analysis; support vector machines; ν-SVC; ν-support vector classification; SVOR learning; equality constraints; finite convergence analysis; incremental support vector learning; inequality constraints; on-line algorithm; ordinal regression problems; quadratic formulation; sum-of-margins strategy; support vector ordinal regression; Algorithm design and analysis; Convergence; Educational institutions; Support vector machines; Training; Training data; Vectors; Incremental learning; online learning; ordinal regression (OR); support vector machine (SVM);
fLanguage :
English
Journal_Title :
Neural Networks and Learning Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
2162-237X
Type :
jour
DOI :
10.1109/TNNLS.2014.2342533
Filename :
6876186
Link To Document :
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