DocumentCode :
2202127
Title :
Active Set Fuzzy Support Vector ϵ-Insensitive Regression Approach
Author :
Singh, Rampal ; Balasundaram, S.
Author_Institution :
Dept. of Comput. Sci., Univ. of Delhi, New Delhi, India
fYear :
2008
fDate :
20-22 Dec. 2008
Firstpage :
879
Lastpage :
883
Abstract :
In this paper a new fuzzy linear support vector machine formulation for regression problems is proposed and solved by the active set computational strategy. In this model, to each input data a fuzzy membership value is associated so that the input data can contribute proportionally to the learning of the decision surface. The proposed method has the advantage that its solution is obtained by solving a system of linear equations at a finite number of times rather than solving a quadratic optimization problem. Numerical experiments have been performed and the results obtained are in close agreement with the exact solution of the problems considered which clearly shows the effectiveness of the method.
Keywords :
fuzzy set theory; learning (artificial intelligence); minimisation; quadratic programming; regression analysis; support vector machines; active set fuzzy support vector ϵ-insensitive regression approach; decision surface learning; fuzzy linear support vector machine formulation; fuzzy membership value; linear equation; quadratic optimization problem; unconstrained minimization problem; Computer science; Equations; Fuzzy set theory; Fuzzy sets; Learning systems; Optimization methods; Quadratic programming; Statistical learning; Support vector machine classification; Support vector machines; Active Set; Fuzzy Support Vector Machines; Regression;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Computer Theory and Engineering, 2008. ICACTE '08. International Conference on
Conference_Location :
Phuket
Print_ISBN :
978-0-7695-3489-3
Type :
conf
DOI :
10.1109/ICACTE.2008.153
Filename :
4737083
Link To Document :
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