基于MapReduce和spark的支持向量机双类分类架构

Mario A. Giraldo, J. Duitama, J. D. Arias-Londoño
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引用次数: 0

摘要

支持向量机(SVM)由于其较高的泛化能力被广泛应用于机器学习中。序列最小优化(SMO)是最流行的实现,它在各种测试问题的训练集大小上介于线性和二次之间。这使得使用支持向量机训练大型数据集具有很高的计算成本。支持向量机在MapReduce和Spark等分布式系统上的实现已经显示出提高计算成本的效率;本文分析了MapReduce和Spark上数据子集大小和映射任务数量对SVM性能的影响。此外,还提出了一个成本模型,作为根据可用硬件和待处理数据设置数据子集大小的有用工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MapReduce and Spark-based architecture for bi-class classification using SVM
Support Vector Machine (SVM) is a classifier widely used in machine learning because of its high generalization capacity. The sequential minimal optimization (SMO) its most popular implementation, scales somewhere between linear and quadratic in the training set size for various test problems. This fact makes using SVM to train large data sets have a high computational cost. SVM implementations on distributed systems such as MapReduce and Spark have shown efficiency to improve computational cost; this paper analyzes how data subset size and number of mapping tasks affects SVM performance on MapReduce and Spark. Also, a cost model as a useful tool for setting data subset size according to available hardware and data to be processed is proposed.
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