PSVM: Parallelizing Support Vector Machines on Distributed Computers Export

@inproceedings{citeulike:3152638, 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 O(n2) to O(np=m), and improves computation time to O(np2=m). Empirical study shows PSVM to be effective. PSVM Open Source is available for download at http://code.google.com/p/psvm/.}, author = {Chang, Edward Y. and Zhu, Kaihua and Wang, Hao and Bai, Hongjie and Li, Jian and Qiu, Zhihuan}, booktitle = {Advances in Neural Information Processing Systems}, citeulike-article-id = {3152638}, posted-at = {2009-10-27 19:47:43}, priority = {2}, title = {PSVM: Parallelizing Support Vector Machines on Distributed Computers}, volume = {20}, year = {2007} }

TY - CONF ID - citeulike:3152638 L3 - citeulike-article-id:3152638 N2 - 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 O(n2) to O(np=m), and improves computation time to O(np2=m). Empirical study shows PSVM to be effective. PSVM Open Source is available for download at http://code.google.com/p/psvm/. TI - PSVM: Parallelizing Support Vector Machines on Distributed Computers VL - 20 T2 - Advances in Neural Information Processing Systems AU - Chang, Edward AU - Zhu, Kaihua AU - Wang, Hao AU - Bai, Hongjie AU - Li, Jian AU - Qiu, Zhihuan PY - 2007/// ER -