@article{AlbZimDogKlo17,
    author = "Alber, Maximilian and Zimmert, Julian and Dogan, Urun and Kloft, Marius",
    title = "Distributed optimization of multi-class SVMs",
    journal = "PLOS ONE",
    publisher = "Public Library of Science",
    year = "2017",
    month = "06",
    volume = "12",
    url = "https://doi.org/10.1371/journal.pone.0178161",
    pdf = "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0178161\&type=printable",
    pages = "1-18",
    abstract = "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.",
    number = "6",
    doi = "10.1371/journal.pone.0178161"
}