Distributed Support Vector Machine Learning

@inproceedings{Armond2008DistributedSV,
  title={Distributed Support Vector Machine Learning},
  author={Armond and C Schneeberger Kenneth},
  year={2008}
}
Support Vector Machines (SVMs) are used for a growing number of applications. A fundamental constraint on SVM learning is the management of the training set. This is because the order of computations goes as the square of the size of the training set. Typically, training sets of 1000 (500 positives and 500 negatives, for example) can be managed on a PC without hard-drive thrashing. Training sets of 10,000 however, simply cannot be managed with PC-based resources. For this reason most SVM… CONTINUE READING

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