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- W2018449938 abstract "The Support Vector Machine (SVM) algorithm is sensitive to the choice of parameter settings, which makes it hard to use by non-experts. It has been shown that meta-learning can be used to support the selection of SVM parameter values. Previous approaches have used general statistical measures as meta-features. Here we propose a new set of meta-features that are based on the kernel matrix. We test them on the problem of setting the width of the Gaussian kernel for regression problems. We obtain significant improvements in comparison to earlier meta-learning results. We expect that with better support in the selection of parameter values, SVM becomes accessible to a wider range of users." @default.
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- W2018449938 date "2006-04-23" @default.
- W2018449938 modified "2023-10-09" @default.
- W2018449938 title "Selecting parameters of SVM using meta-learning and kernel matrix-based meta-features" @default.
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- W2018449938 doi "https://doi.org/10.1145/1141277.1141408" @default.
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