Platt, J. (1999b). Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In Smola, A., Bartlett, P., Scholkopf, B., and Schuurmans, D., editors, Advances in Large Margin Classi ers. MIT Press. Home/Search Document Details and Download
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....codes, we can simplify equation (2) to P (O s = m qs f s ) #. 3) In this case the decoding function will be: d(m q , f ) log P (Y = q f ) 4) The problem boils down to estimating the individual conditional probabilities in Eq. 3) a problem that has been addressed also in [20] [21]. Our solution consists of fitting the following set of parametric models: P (O s = m qs f s ) 1 1 exp m qs (A s f s B s ) where A s and B s are adjustable real parameters that reflect the slope and the offset of the cumulative distribution of the margins. A s and B s can be estimated ....
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J. Platt, "Probabilistic outputs for support vector machines and comparison to regularized likelihood methods," in Advances in Large Margin Classiers, A. Smola, P. Bartlett, B. Scholkopf, and D. Schurmans, Eds. MIT Press, 1999.
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Platt, J. (1999b). Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In Smola, A., Bartlett, P., Scholkopf, B., and Schuurmans, D., editors, Advances in Large Margin Classi ers. MIT Press.
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