A Parallel Mixture of SVMs for Very Large Scale Problems

  title={A Parallel Mixture of SVMs for Very Large Scale Problems},
  author={Ronan Collobert and Samy Bengio and Yoshua Bengio},
  journal={Neural Computation},
Support vector machines (SVMs) are the state-of-the-art models for many classification problems, but they suffer from the complexity of their training algorithm, which is at least quadratic with respect to the number of examples. Hence, it is hopeless to try to solve real-life problems having more than a few hundred thousand examples with SVMs. This article proposes a new mixture of SVMs that can be easily implemented in parallel and where each SVM is trained on a small subset of the whole data… CONTINUE READING
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