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- W2127163829 abstract "Linear classifiers have been shown to be effective for many discrimination tasks. Irrespective of the learning algorithm itself, the final classifier has a weight to multiply by each feature. This suggests that ideally each input feature should be linearly correlated with the target variable (or anti-correlated), whereas raw features may be highly non-linear. In this paper, we attempt to re-shape each input feature so that it is appropriate to use with a linear weight and to scale the different features in proportion to their predictive value. We demonstrate that this pre-processing is beneficial for linear SVM classifiers on a large benchmark of text classification tasks as well as UCI datasets." @default.
- W2127163829 created "2016-06-24" @default.
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- W2127163829 date "2009-06-28" @default.
- W2127163829 modified "2023-10-06" @default.
- W2127163829 title "Feature shaping for linear SVM classifiers" @default.
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- W2127163829 doi "https://doi.org/10.1145/1557019.1557057" @default.
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