2026-09-19T07:07:55Zhttps://wakespace.lib.wfu.edu/server/oai/request

oai:null:10339/593152026-09-02T13:35:01Zcom_10339_14934col_10339_38132

Patel, Udita 2016-05-21T08:35:51Z 2018-05-20T08:30:10Z 2016 https://wakespace.lib.wfu.edu/handle/10339/59315 The quantity of electronic data available for analysis has grown exponentially with the rapid development of the World Wide Web, the Internet of Things, and other digital technologies. As a result, data mining and machine learning algorithms face computational complexity issues when applied to real world datasets. Support Vector Machines (SVM) are powerful classification and regression tools but their computational requirements increase rapidly as the number of training examples increases. To address this problem, several parallel MapReduce based implementations of SVMs have been proposed. These implementation have in common that they decompose a large-scale multi-class problem to a number of relatively smaller subproblems by dividing the data into multiple partitions which can be processed in parallel; however, these approaches use different aggregation and combination strategies to form the final model. en Wake Forest University Machine Learning Mapreduce MNIST Parallel Support Vector Machine SVM Udita Patel Performance Analysis of Parallel Support Vector Machines on a MapReduce Architecture Thesis Thomas, Stan J Turkett, William H Computer Science 2018-05-20 open.access