Machine Learning Toolkit for Extreme Scale
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
- Short Name / Acronym:
- MATEX; 002974WKSTN00
- Version:
- 00
- Programming Language(s):
- Medium: X; OS: Linux; Compatibility: Workstation
- Research Organization:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- Contributing Organization:
- Abhinav Vishnu PNNL Jeyanthi Narasmihan (student intern) Washington State University Khushbu Aqarwal PNNL
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1231737
- Country of Origin:
- United States
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