The University of Southampton
University of Southampton Institutional Repository

Memory-efficient large-scale linear support vector machine

Record type: Conference or Workshop Item (Paper)

Stochastic gradient descent has been advanced as a computationally efficient method for large-scale problems. In classification problems, many proposed linear support vector machines as very effective classifiers. However, they assume that the data is already in memory which might not be always the case. Recent work suggests a classical method that divides such a problem into smaller blocks and then solves the sub-problems iteratively. We show that a simple modification of shrinking the dataset early will produce significant saving in computation and memory. We further ?nd that on problems larger than previously considered, our approach is able to reach solutions on top-end desktop machines while competing methods cannot.

PDF icmv14.pdf - Author's Original
Download (300kB)
PDF svm.pdf - Other
Download (309kB)

Citation

Alrajeh, Abdullah, Takeda, Akiko and Niranjan, Mahesan (2014) Memory-efficient large-scale linear support vector machine At The 7th International Conference on Machine Vision, Italy. 19 - 21 Nov 2014.

More information

Published date: November 2014
Venue - Dates: The 7th International Conference on Machine Vision, Italy, 2014-11-19 - 2014-11-21
Related URLs:
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 368208
URI: http://eprints.soton.ac.uk/id/eprint/368208
PURE UUID: 0bd228fe-5db4-43fe-a99e-534039190ef2

Catalogue record

Date deposited: 20 Aug 2014 15:26
Last modified: 18 Jul 2017 01:50

Export record

Contributors

Author: Abdullah Alrajeh
Author: Akiko Takeda

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×