Support Vector Machines (SVM) is a popular machine learning technique, which has been applied to a wide range of domains such as science, finance, and social networks for supervised learning. MaTEx undertakes the challenge of designing a scalable parallel SVM training algorithm for large scale systems, which includes commodity multi-core machines, tightly connected supercomputers and cloud computing systems. Several techniques are proposed for improved speed and memory space usage including adaptive and aggressive elimination of samples for faster convergence , and sparse format representation of data samples. Several heuristics for earliest possible to lazy elimination of non-contributing samples are considered in MaTEx. In many cases, where an early sample elimination might result in a false positive, low overhead mechanisms for reconstruction of key data structures are proposed. The proposed algorithm and heuristics are implemented and evaluated on various publicly available datasets
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Open Source Software package available from Pacific Northwest National Laboratory at the following URL: http://hpc.pnl.gov/projects/matex
Source Code Available:
Pacific Northwest National Laboratory
United States Department of Energy
Abhinav Vishnu PNNL Jeyanthi Narasmihan (student intern) Washington State University Khushbu Aqarwal PNNL
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Software Package Details
Title: Machine Learning Toolkit for Extreme Scale
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