%Aigaion2 BibTeX export from Idiap Publications
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@TECHREPORT{pozd05-32,
         author = {Pozdnoukhov, Alexei and Bengio, Samy},
       projects = {Idiap},
          title = {A Kernel Classifier for Distributions},
           type = {Idiap-RR},
         number = {Idiap-RR-32-2005},
           year = {2005},
    institution = {IDIAP},
           note = {Submitted to NIPS},
       abstract = {This paper presents a new algorithm for classifying distributions. The algorithm combines the principle of margin maximization and a kernel trick, applied to distributions. Thus, it combines the discriminative power of support vector machines and the well-developed framework of generative models. It can be applied to a number of real-life tasks which include data represented as distributions. The algorithm can also be applied for introducing some prior knowledge on invariances into a discriminative model. We illustrate this approach in details for the case of Gaussian distributions, using a toy problem. We also present experiments devoted to the real-life problem of invariant image classification.},
            pdf = {https://publications.idiap.ch/attachments/reports/2005/rr05-32.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2005/rr05-32.ps.gz},
ipdmembership={learning},
}