Support Vector Machines On Distributed Computers - 1452 Words

PSVM: Parallelizing Support Vector Machines on Distributed Computers Edward Y. Changâˆâ€", Kaihua Zhu, Hao Wang, Hongjie Bai, Jian Li, Zhihuan Qiu, Hang Cui Google Research, Beijing, China 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/. 1 Introduction Let us examine the resource bottlenecks of SVMs in a binary classification setting to explain our proposed solution. Given a set of training data X = {(xi, yi)|xi ∈ Rd}ni=1, where xi is an obser- vation vector, yi ∈ {−1,1} is the class label of xi, and n is the size of X, we apply SVMs on X to train a binary classifier. SVMs aim to search a hyperplane in the Reproducing Kernel Hilbert Space (RKHS) that maximizes the margin between the two classes of data in X with the smallest train- ing error (Vapnik, 1995). ThisShow MoreRelatedParallel Support Vector Machines Is A Supervised Machine Learning Alogrithom Used For Classification1158 Words   |  5 Pages Parallel Support Vector Machine Junfeng Wu Junming Chen May 6, 2016 1 INTRODUCTION Support vector machines is a supervised machine learning alogrithom used for classification. The problem could be written : minimize 1 |w |2 2 yi((w,xi)+b)−1≠¥0 where w is a linear combination of the training data: n w = ÃŽ ±i k(xi ) i=1 this could be further written in a dual form[5]: min 1ÃŽ ±TQÃŽ ±Ã¢Ë†â€™eTÃŽ ± ÃŽ ±2 0≠¤ÃŽ ±i ≠¤C, yTÃŽ ±=0, ∀i ≠¤n where Q is the kernel matrix. This dual form is a quadratic programming problem with linearRead MoreDetection Ratio Of Cyber Attack Detection2009 Words   |  9 Pages Improved the Detection Ratio of Cyber Attack Using Feature Reduction Based on Support Vector Machine and Glowworm Optimization Abstract— The swarm intelligence plays vital role in feature reduction process in cyber-attack detection. The family of swarm intelligence gives bucket of algorithm for the processing of feature reduction such as ant colony optimization, particle swarm optimization and many more. 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