Adapting Codes and Embeddings for Polychotomies
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. Differ- ently 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 two- class classifiers, similar in spirit to Boosting.
Cite
Text
Rätsch et al. "Adapting Codes and Embeddings for Polychotomies." Neural Information Processing Systems, 2002.Markdown
[Rätsch et al. "Adapting Codes and Embeddings for Polychotomies." Neural Information Processing Systems, 2002.](https://mlanthology.org/neurips/2002/ratsch2002neurips-adapting/)BibTeX
@inproceedings{ratsch2002neurips-adapting,
title = {{Adapting Codes and Embeddings for Polychotomies}},
author = {Rätsch, Gunnar and Mika, Sebastian and Smola, Alex J.},
booktitle = {Neural Information Processing Systems},
year = {2002},
pages = {529-536},
url = {https://mlanthology.org/neurips/2002/ratsch2002neurips-adapting/}
}