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.
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