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- W2149221247 abstract "We examine support vector machines (SVM) from the point of view of solutions to variational problems in a reproducing kernel Hilbert space. We discuss the generalized comparative Kullback-Leibler distance as a target for choosing tuning parameters in SVMs, and we propose that the generalized approximate cross validation estimate of them is a reasonable proxy for this target. We indicate an interesting relationship between the generalized approximate cross validation and the SVM margin." @default.
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- W2149221247 date "2003-01-20" @default.
- W2149221247 modified "2023-09-24" @default.
- W2149221247 title "Margin-like quantities and generalized approximate cross validation for support vector machines" @default.
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- W2149221247 doi "https://doi.org/10.1109/nnsp.1999.788118" @default.
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