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Poster

Parallelizing Support Vector Machines on Distributed Computers

Edward Y Chang · Kaihua Zhu · Hao Wang · hongjie Bai · Jian Li · Zhihuan Qiu · Hang Cui

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2007 Poster

Abstract: Support Vector Machines (SVMs) suffer from a widely recognized scalability problem in both memory use and computational time. To improve scalability, we have developed a parallel SVM algorithm (PSVM), which reduces memory use through performing a row-based, approximate matrix factorization, and which loads only essential data to each machine to perform parallel computation. Let n denote the number of training instances, p the reduced matrix dimension after factorization (p is significantly smaller than n), and m the number of machines. PSVM reduces the memory requirement from \MO(n2) to \MO(np/m), and improves computation time to \MO(np2/m). Empirical studies on up to 500 computers shows PSVM to be effective.

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