loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Sangkyun Lee 1 and Stephen Wright 2

Affiliations: 1 University of Technology, Germany ; 2 University of Wisconsin, United States

Keyword(s): Stochastic approximation, Large-scale, Online learning, Support vector machines, Nonlinear kernels.

Related Ontology Subjects/Areas/Topics: Convex Optimization ; Kernel Methods ; Large Margin Methods ; On-Line Learning ; Pattern Recognition ; Stochastic Methods ; Theory and Methods

Abstract: Subgradient methods for training support vector machines have been quite successful for solving large-scale and online learning problems. However, they have been restricted to linear kernels and strongly convex formulations. This paper describes efficient subgradient approaches without such limitations, making use of randomized low-dimensional approximations to nonlinear kernels, and minimization of a reduced primal formulation using an algorithm based on robust stochastic approximation, which do not require strong convexity.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 44.201.59.20

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Lee, S. and Wright, S. (2012). ASSET: APPROXIMATE STOCHASTIC SUBGRADIENT ESTIMATION TRAINING FOR SUPPORT VECTOR MACHINES. In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM; ISBN 978-989-8425-98-0; ISSN 2184-4313, SciTePress, pages 223-228. DOI: 10.5220/0003786202230228

@conference{icpram12,
author={Sangkyun Lee. and Stephen Wright.},
title={ASSET: APPROXIMATE STOCHASTIC SUBGRADIENT ESTIMATION TRAINING FOR SUPPORT VECTOR MACHINES},
booktitle={Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM},
year={2012},
pages={223-228},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003786202230228},
isbn={978-989-8425-98-0},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM
TI - ASSET: APPROXIMATE STOCHASTIC SUBGRADIENT ESTIMATION TRAINING FOR SUPPORT VECTOR MACHINES
SN - 978-989-8425-98-0
IS - 2184-4313
AU - Lee, S.
AU - Wright, S.
PY - 2012
SP - 223
EP - 228
DO - 10.5220/0003786202230228
PB - SciTePress