| 摘要 |
The time taken to learn a model from training examples is often unacceptable. For instance, training language understanding models with Adaboost or SVMs can take weeks or longer based on numerous training examples. Parallelization through the use of multiple processors may improve learning speed. The disclosure describes effective systems for distributed multiclass classification learning on several processors. These systems are applicable to multiclass models where the training process may be split into training of independent binary classifiers.
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