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 [BibTeX] [Marc21]
A Kernel Classifier for Distributions
Type of publication: Idiap-RR
Citation: pozd05-32
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.
Userfields: ipdmembership={learning},
Keywords:
Projects Idiap
Authors Pozdnoukhov, Alexei
Bengio, Samy
Added by: [UNK]
Total mark: 0
Attachments
  • rr05-32.pdf
  • rr05-32.ps.gz
Notes