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Asymptotically optimal choice of varepsilon-loss for support vector machines

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Citation

Smola, A., Murata, N., Schölkopf, B., & Müller, K.-R. (1998). Asymptotically optimal choice of varepsilon-loss for support vector machines. In L. Niklasson, M. Boden, & T. Ziemke (Eds.), ICANN 98: 8th International Conference on Artificial Neural Networks, Skövde, Sweden, 2–4 September 1998 (pp. 105-110). Berlin, Germany: Springer.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-E94A-6
Abstract
Under the assumption of asymptotically unbiased estimators we show that there exists a nontrivial choice of the insensitivity parameter in Vapnik’s ε-insensitive loss function which scales linearly with the input noise of the training data. This finding is backed by experimental results.