NDLI: A nonlinear least squares quasi-Newton strategy for LP-SVR hyper-parameters selection

Content Provider	Springer Nature Link
Author	Rivas Perea, Pablo Cota Ruiz, Juan Rosiles, Jose Gerardo
Copyright Year	2013
Abstract	This paper studies the problem of hyper-parameters selection for a linear programming-based support vector machine for regression (LP-SVR). The proposed model is a generalized method that minimizes a linear-least squares problem using a globalization strategy, inexact computation of first order information, and an existing analytical method for estimating the initial point in the hyper-parameters space. The minimization problem consists of finding the set of hyper-parameters that minimizes any generalization error function for different problems. Particularly, this research explores the case of two-class, multi-class, and regression problems. Simulation results among standard data sets suggest that the algorithm achieves statistically insignificant variability when measuring the residual error; and when compared to other methods for hyper-parameters search, the proposed method produces the lowest root mean squared error in most cases. Experimental analysis suggests that the proposed approach is better suited for large-scale applications for the particular case of an LP-SVR. Moreover, due to its mathematical formulation, the proposed method can be extended in order to estimate any number of hyper-parameters.
Starting Page	579
Ending Page	597
Page Count	19
File Format	PDF
ISSN	18688071
Journal	International Journal of Machine Learning and Cybernetics
Volume Number	5
Issue Number	4
e-ISSN	1868808X
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-02-27
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Support vector regression Hyper-parameters Large-scale LP-SVR Computational Intelligence Artificial Intelligence (incl. Robotics) Control, Robotics, Mechatronics Statistical Physics, Dynamical Systems and Complexity Systems Biology Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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