NDLI: Revised Optimizer of SVR Hyperparameters Minimizing Cross-Validation Error

Content Provider	IEEE Xplore Digital Library
Author	Karasuyama, M. Kitakoshi, D. Nakano, R.
Copyright Year	2006
Description	Author affiliation: Nagoya Inst. of Technol., Nagoya (Karasuyama, M.; Kitakoshi, D.; Nakano, R.)
Abstract	The performance of support vector regression (SVR) deeply depends on its hyperparameters such as an insensitive zone thickness epsiv, a penalty factor C, and RBF kernel parameter sigma. A method called MCV-SVR was once proposed, which optimizes SVR hyperparameters so that a cross-validation error is minimized. However, as pointed out in this paper, the MCV-SVR (or its variants) has numerical instability in gradient calculation, which may cause bad influence on performance. Thus, this paper introduces a new method of computing the gradient of parameters with respect to hyperparameters. The revised optimizers incorporating the new method is shown to be free from the instability problem. Our experiments using three data sets showed that the revised optimizers considerably improved generalization performance of the MCV-SVR or its variant, and outperformed other methods such as multi-layer perceptrons or SVR with practical setting of hyperparameters.
Starting Page	319
Ending Page	326
File Size	467643
Page Count	8
File Format	PDF
ISBN	0780394909
DOI	10.1109/IJCNN.2006.246698
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Optimization methods Kernel Multilayer perceptrons Support vector machines Support vector machine classification Pattern classification Training data Neural networks Computer science Lagrangian functions
Content Type	Text
Resource Type	Article

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