On Coresets for Support Vector Machines

Murad Tukan, Cenk Baykal, Dan Feldman, Daniela Rus

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationTheory and Applications of Models of Computation - 16th International Conference, TAMC 2020, Proceedings
EditorsJianer Chen, Qilong Feng, Jinhui Xu
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)9783030592660
StatePublished - 2020
Event16th Annual Conference on Theory and Applications of Models of Computation, TAMC 2020 - Changsha, China
Duration: 18 Oct 202020 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12337 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th Annual Conference on Theory and Applications of Models of Computation, TAMC 2020

Bibliographical note

Funding Information:
This research was supported in part by the U.S. National Science Foundation (NSF) under Awards 1723943 and 1526815, Office of Naval Research (ONR) Grant N00014-18-1-2830, Microsoft, and JP Morgan Chase. M. Tukan and C. Baykal—These authors contributed equally to this work.

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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