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- W2615757843 abstract "In using the Ɛ-support vector regression (Ɛ-SVR) algorithm, one has to decide on a suitable value of the insensitivity parameter Ɛ. Smola et al. [6] determined its choice based on maximizing the statistical efficiency of a location parameter estimator. While they successfully predicted a linear scaling between the optimal Ɛ and the noise in the data, the value of the theoretically optimal Ɛ does not have a close match with its experimentally observed counterpart. In this paper, we attempt to better explain the experimental results there, by analyzing a toy problem with a closer setting to the Ɛ-SVR. Our resultant predicted choice of Ɛ is much closer to the experimentally observed value, while still demonstrating a linear trend with the data noise." @default.
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- W2615757843 date "2001-01-01" @default.
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- W2615757843 title "Linear dependency between epsilon and the input noise in epsilon-support vector regression" @default.
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