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To speed up the training process, many existing systems use paralleltechnology for online learning algorithms. However, most research mainly focuson stochastic gradient descent (SGD) instead of other algorithms. We propose ageneric online parallel learning framework for large margin models, and alsoanalyze our framework on popular large margin algorithms, including MIRA andStructured Perceptron. Our framework is lock-free and easy to implement onexisting systems. Experiments show that systems with our framework can gainnear linear speed up by increasing running threads, and with no loss inaccuracy.

A Generic Online Parallel Learning Framework for Large Margin Models