Solving Multiclass Support Vector Machines with LaRank
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.
Cite
Text
Bordes et al. "Solving Multiclass Support Vector Machines with LaRank." International Conference on Machine Learning, 2007. doi:10.1145/1273496.1273508Markdown
[Bordes et al. "Solving Multiclass Support Vector Machines with LaRank." International Conference on Machine Learning, 2007.](https://mlanthology.org/icml/2007/bordes2007icml-solving/) doi:10.1145/1273496.1273508BibTeX
@inproceedings{bordes2007icml-solving,
title = {{Solving Multiclass Support Vector Machines with LaRank}},
author = {Bordes, Antoine and Bottou, Léon and Gallinari, Patrick and Weston, Jason},
booktitle = {International Conference on Machine Learning},
year = {2007},
pages = {89-96},
doi = {10.1145/1273496.1273508},
url = {https://mlanthology.org/icml/2007/bordes2007icml-solving/}
}