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 | |
| Added by: | [UNK] |
| Total mark: | 0 |
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