Training of one-vs.-rest SVMs can be parallelized over the number of classes in a straight forward way. Given enough computational resources, one-vs.-rest SVMs can thus be trained on data involving a large number of classes. The same cannot be stated, however, for the so-called all-in-one SVMs, which require solving a quadratic program of size quadratically in the number of classes. We develop distributed algorithms for two all-in-one SVM formulations (Lee et al. and Weston and Watkins) that parallelize the computation evenly over the number of classes. This allows us to compare these models to one-vs.-rest SVMs on unprecedented scale. The results indicate superior accuracy on text classification data.
@article{arxiv.1611.08480,
title = {Distributed Optimization of Multi-Class SVMs},
author = {Maximilian Alber and Julian Zimmert and Urun Dogan and Marius Kloft},
journal= {arXiv preprint arXiv:1611.08480},
year = {2017}
}