On Coresets for Support Vector Machines
Name
2002.06469.pdf
Description
Submitted version
Size
1.15 MB
Format
Adobe PDF
Checksum (MD5)
eddd5e6b104ac53eac9a688305ceb918
Author(s)
Baykal, Cenk
Rus, Daniela L
Date Issued
October 2020
Journal
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher
Springer International Publishing
Citation
Tukan, Murad et al. “On Coresets for Support Vector Machines.” Paper in the Lecture Notes in Computer Science, 12337 LNCS, International Conference on Theory and Applications of Models of Computation (TAMC 2020), Changsha, China, 18-20 Oct, 2020, Springer International Publishing: 287-299 © 2020 The Author(s)
Version
Original manuscript
Abstract
We present an efficient coreset construction algorithm for large-scale Support Vector Machine (SVM) training in Big Data and streaming applications. A coreset is a small, representative subset of the original data points such that a models trained on the coreset are provably competitive with those trained on the original data set. Since the size of the coreset is generally much smaller than the original set, our preprocess-then-train scheme has potential to lead to significant speedups when training SVM models. We prove lower and upper bounds on the size of the coreset required to obtain small data summaries for the SVM problem. As a corollary, we show that our algorithm can be used to extend the applicability of any off-the-shelf SVM solver to streaming, distributed, and dynamic data settings. We evaluate the performance of our algorithm on real-world and synthetic data sets. Our experimental results reaffirm the favorable theoretical properties of our algorithm and demonstrate its practical effectiveness in accelerating SVM training.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/978-3-030-59267-7_25
