*Abstract*:
Optimization algorithms for large margin multiclass recognizers
are often too costly to handle ambitious problems with
structured outputs and exponential numbers of classes.
Optimization algorithms that rely on the full gradient
are not effective because, unlike the solution,
the gradient is not sparse and is very large.
The `LaRank`

algorithm sidesteps this difficulty
by relying on a randomized exploration inspired by the perceptron
algorithm. We show that this approach is competitive with gradient
based optimizers on simple multiclass problems.
Furthermore, a single `LaRank`

pass over the training
examples delivers test error rates that are nearly as good
as those of the final solution.

Antoine Bordes, Léon Bottou, Patrick Gallinari and Jason Weston: **Solving MultiClass Support Vector Machines with LaRank**, *Proceedings of the 24th International Machine Learning Conference*, 89-96, Edited by Zoubin Ghahramani, OmniPress, Corvallis, Oregon, 2007.

@inproceedings{bordes-2007, author = {Bordes, Antoine and Bottou, L\'{e}on and Gallinari, Patrick and Weston, Jason}, title = {Solving MultiClass Support Vector Machines with LaRank}, booktitle = {Proceedings of the 24th International Machine Learning Conference}, pages = {89-96}, year = {2007}, editor = {Ghahramani, Zoubin}, address = {Corvallis, Oregon}, publisher = {OmniPress}, url = {http://leon.bottou.org/papers/bordes-2007}, }

Antoine Bordes provides an implementation of the LaRank algorithm. This new implementation runs slightly faster than the code we have used for the paper. In addition there is a special version for the case of linear kernels.