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Parallel training of a Support Vector Machine (SVM) with distributed block minimization
Parallel training of a Support Vector Machine (SVM) with distributed block minimization
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机译:支持向量机(SVM)的并行训练与分布式块最小化
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摘要
A method to solve large scale linear SVM that is efficient in terms of computation, data storage and communication requirements. The approach works efficiently over very large datasets, and it does not require any master node to keep any examples in its memory. The algorithm assumes that the dataset is partitioned over several nodes on a cluster, and it performs “distributed block minimization” to achieve the desired results. Using the described approach, the communication complexity of the algorithm is independent of the number of training examples.
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