Asymptotically Optimal Choice of ε-Loss for Support Vector Machines

Smola, AJ; Murata, N; Schölkopf, B; Müller, K-R

doi:10.1007/978-1-4471-1599-1_11

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Asymptotically Optimal Choice of ε-Loss for Support Vector Machines

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Smola, A., Murata, N., Schölkopf, B., & Müller, K.-R. (1998). Asymptotically Optimal Choice of ε-Loss for Support Vector Machines. In L. Niklasson, M. Bodén, & T. Ziemke (Eds.), ICANN 98: 8th International Conference on Artificial Neural Networks, Skövde, Sweden, 2–4 September 1998 (pp. 105-110). London, UK: Springer.

Cite as: https://hdl.handle.net/21.11116/0000-0005-DBC8-3

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.