### Abstract

AGEL-SVM is an extension to a kernel Support Vector Machine (SVM) and is designed for distributed computing using Approximate Global Exhaustive Local sampling (AGEL)-SVM. The dual form of SVM is typically solved using sequential minimal optimization (SMO) which iterates very fast if the full kernel matrix can fit in a computer’s memory. AGEL-SVM aims to partition the feature space into sub problems such that the kernel matrix per problem can fit in memory by approximating the data outside each partition. AGEL-SVM has similar Cohen’s Kappa and accuracy metrics as the underlying SMO implementation. AGEL-SVM’s training times greatly decreased when running on a 128 worker MATLAB pool on Amazon’s EC2. Predictor evaluation times are also faster due to a reduction in support vectors per partition.

Original language | English |
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Title of host publication | BDCAT 2017 - Proceedings of the 4th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies |

Publisher | Association for Computing Machinery, Inc |

Pages | 267-268 |

Number of pages | 2 |

ISBN (Electronic) | 9781450355490 |

DOIs | |

State | Published - Dec 5 2017 |

Event | 4th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2017 - Austin, United States Duration: Dec 5 2017 → Dec 8 2017 |

### Publication series

Name | BDCAT 2017 - Proceedings of the 4th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies |
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### Conference

Conference | 4th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2017 |
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Country | United States |

City | Austin |

Period | 12/5/17 → 12/8/17 |

### Keywords

- AGEL
- AGEL-SVM
- AMAZON
- Distributed
- EC2
- Kernel
- MATLAB
- SVM

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## Cite this

*BDCAT 2017 - Proceedings of the 4th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies*(pp. 267-268). (BDCAT 2017 - Proceedings of the 4th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies). Association for Computing Machinery, Inc. https://doi.org/10.1145/3148055.3149206