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Conference Paper

Adapting Codes and Embeddings for Polychotomies

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84153

Rätsch,  G
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Rätsch, G., Smola, A., & Mika, S. (2003). Adapting Codes and Embeddings for Polychotomies. Advances in Neural Information Processing Systems, 513-520.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-DB33-7
Abstract
In this paper we consider formulations of multi-class problems based on a generalized notion of a margin and using output coding. This includes, but is not restricted to, standard multi-class SVM formulations. Differently from many previous approaches we learn the code as well as the embedding function. We illustrate how this can lead to a formulation that allows for solving a wider range of problems with for instance many classes or even “missing classes”. To keep our optimization problems tractable we propose an algorithm capable of solving them using twoclass classifiers, similar in spirit to Boosting.