%Aigaion2 BibTeX export from Idiap Publications
%Thursday 18 July 2024 04:51:48 PM

@INPROCEEDINGS{grandvalet:nips:2005,
         author = {Grandvalet, Yves and Mari{\'{e}}thoz, Johnny and Bengio, Samy},
       projects = {Idiap},
          title = {A Probabilistic Interpretation of SVMs with an Application to Unbalanced Classification},
      booktitle = {Advances in Neural Information Processing Systems, {NIPS} 15},
           year = {2005},
           note = {IDIAP-RR 05-26},
       crossref = {grandvalet:rr05-26},
       abstract = {In this paper, we show that the hinge loss can be interpreted as the neg-log-likelihood of a semi-parametric model of posterior probabilities. From this point of view, SVMs represent the parametric component of a semi-parametric model fitted by a maximum a posteriori estimation procedure. This connection enables to derive a mapping from SVM scores to estimated posterior probabilities. Unlike previous proposals, the suggested mapping is interval-valued, providing a set of posterior probabilities compatible with each SVM score. This framework offers a new way to adapt the SVM optimization problem when decisions result in unequal losses. Experiments on an unbalanced classification loss show improvements over state-of-the-art procedures.},
            pdf = {https://publications.idiap.ch/attachments/papers/2005/grandvalet-nips-2005.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/papers/2005/grandvalet-nips-2005.ps.gz},
ipdmembership={learning},
}



crossreferenced publications: 
@TECHREPORT{grandvalet:rr05-26,
         author = {Grandvalet, Yves and Mari{\'{e}}thoz, Johnny and Bengio, Samy},
       projects = {Idiap},
          title = {A Probabilistic Interpretation of SVMs with an Application to Unbalanced Classification},
           type = {Idiap-RR},
         number = {Idiap-RR-26-2005},
           year = {2005},
    institution = {IDIAP},
           note = {Published in Advances in Neural Information Processing Systems, {NIPS} 15, 2005},
       abstract = {In this paper, we show that the hinge loss can be interpreted as the neg-log-likelihood of a semi-parametric model of posterior probabilities. From this point of view, SVMs represent the parametric component of a semi-parametric model fitted by a maximum a posteriori estimation procedure. This connection enables to derive a mapping from SVM scores to estimated posterior probabilities. Unlike previous proposals, the suggested mapping is interval-valued, providing a set of posterior probabilities compatible with each SVM score. This framework offers a new way to adapt the SVM optimization problem when decisions result in unequal losses. Experiments on an unbalanced classification loss show improvements over state-of-the-art procedures.},
            pdf = {https://publications.idiap.ch/attachments/reports/2005/grandvalet-idiap-rr-05-26.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2005/grandvalet-idiap-rr-05-26.ps.gz},
ipdmembership={learning},
}