NDLI: A regularization on Lagrangian twin support vector regression

Content Provider	SpringerLink
Author	Tanveer, M. Shubham, K.
Copyright Year	2015
Abstract	Twin support vector regression (TSVR), Lagrangian TSVR (LTSVR) and $$\epsilon$$ -TSVR obtain good generalization and faster computational speed by solving a pair of smaller sized quadratic programming problems (QPPs) than a single large QPP in support vector regression (SVR). In this paper, a simple and linearly convergent Lagrangian support vector machine algorithm for the dual of the $$\epsilon$$ -TSVR is proposed. The contributions of our formulation are as follows: (1) we consider the square of the 2-norm of the vector of slack variables instead of the usual 1-norm to make the objective functions strongly convex. (2) We are solving regression problem with just two systems of linear equations as opposed to solving two QPPs in $$\epsilon$$ -TSVR and TSVR or one large QPP in SVR, which leads to extremely simple and fast algorithm. (3) One significant advantage of our proposed method is the implementation of structural risk minimization principle. However, only empirical risk is considered in the primal problems of TSVR and LTSVR due to its complex structure and thus may incur overfitting and suboptimal in some cases. (4) The experimental results on several artificial and benchmark datasets show the effectiveness of our proposed formulation.
Starting Page	807
Ending Page	821
Page Count	15
File Format	PDF
ISSN	18688071
Journal	International Journal of Machine Learning and Cybernetics
Volume Number	8
Issue Number	3
e-ISSN	1868808X
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2015-05-01
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Machine learning Lagrangian support vector machines Twin support vector regression Iterative method Computational Intelligence Artificial Intelligence (incl. Robotics) Control, Robotics, Mechatronics Complex Systems Systems Biology Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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