Support Vector Regression with Automatic Accuracy Control
A new algorithm for Support Vector regression is proposed. For a priori chosen ν, it automatically adjusts a flexible tube of minimal radius to the data such that at most a fraction ν of the data points lie outside. The algorithm is analysed theoretically and experimentally.
KeywordsSupport Vector Support Vector Regression Dual Variable Sinc Function Standard Deviation Error
Unable to display preview. Download preview PDF.
- B. E. Boser, I. M. Guyon, and V. N. Vapnik. A training algorithm for optimal margin classifiers. In D. Haussler, editor, Proc. 5th Ann. ACM Workshop on COLT, pages 144–152, Pittsburgh, PA, July 1992. ACM Press.Google Scholar
- B. Schölkopf, C. Burges, and V. Vapnik. Extracting support data for a given task. In U. M. Fayyad and R. Uthurusamy, editors, Proceedings, First International Conference on Knowledge Discovery & Data Mining. AAAI Press, Menlo Park, CA, 1995.Google Scholar
- A. Smola, N. Murata, B. Schölkopf, and K.-R. Müller. Asymptotically optimal choice of ε-loss for support vector machines. ICANN’98.Google Scholar