@inproceedings{ae2486398b0042b4ae5cbc1313144c17,
title = "A kernel support vector machine trained using approximate global and exhaustive local sampling",
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{\textquoteright}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{\textquoteright}s Kappa and accuracy metrics as the underlying SMO implementation. AGEL-SVM{\textquoteright}s training times greatly decreased when running on a 128 worker MATLAB pool on Amazon{\textquoteright}s EC2. Predictor evaluation times are also faster due to a reduction in support vectors per partition.",
keywords = "AGEL, AGEL-SVM, AMAZON, Distributed, EC2, Kernel, MATLAB, SVM",
author = "Benjamin Bryant and Hamed Sari-Sarraf and Rodney Long and Sameer Antani",
note = "Publisher Copyright: {\textcopyright} 2017 Copyright is held by the owner/author(s).; 4th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2017 ; Conference date: 05-12-2017 Through 08-12-2017",
year = "2017",
month = dec,
day = "5",
doi = "10.1145/3148055.3149206",
language = "English",
series = "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",
booktitle = "BDCAT 2017 - Proceedings of the 4th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies",
}