research-article

Optimized cutting plane algorithm for support vector machines

Authors:

Vojtěch Franc,

Soeren SonnenburgAuthors Info & Claims

ICML '08: Proceedings of the 25th international conference on Machine learning

Pages 320 - 327

https://doi.org/10.1145/1390156.1390197

Published: 05 July 2008 Publication History

Abstract

We have developed a new Linear Support Vector Machine (SVM) training algorithm called OCAS. Its computational effort scales linearly with the sample size. In an extensive empirical evaluation OCAS significantly outperforms current state of the art SVM solvers, like SVM^light, SVM^perf and BMRM, achieving speedups of over 1,000 on some datasets over SVM^light and 20 over SVM^perf, while obtaining the same precise Support Vector solution. OCAS even in the early optimization steps shows often faster convergence than the so far in this domain prevailing approximative methods SGD and Pegasos. Effectively parallelizing OCAS we were able to train on a dataset of size 15 million examples (itself about 32GB in size) in just 671 seconds --- a competing string kernel SVM required 97,484 seconds to train on 10 million examples sub-sampled from this dataset.

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Cited By

Menon RChakrabarti I(2023)Low complexity VLSI architecture for improved primal–dual support vector machine learning coreMicroprocessors and Microsystems10.1016/j.micpro.2023.10480698(104806)Online publication date: Apr-2023
https://doi.org/10.1016/j.micpro.2023.104806
Piccialli VSciandrone M(2022)Nonlinear optimization and support vector machinesAnnals of Operations Research10.1007/s10479-022-04655-x314:1(15-47)Online publication date: 14-Apr-2022
https://doi.org/10.1007/s10479-022-04655-x
Ju XYan ZWang T(2021)Overview of Optimization Algorithms for Large-scale Support Vector Machines2021 International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW53433.2021.00119(909-916)Online publication date: Dec-2021
https://doi.org/10.1109/ICDMW53433.2021.00119
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Optimized cutting plane algorithm for support vector machines

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Information

Published In

cover image ACM Other conferences

ICML '08: Proceedings of the 25th international conference on Machine learning

July 2008

1310 pages

ISBN:9781605582054

DOI:10.1145/1390156

General Chair:
William Cohen
Carnegie Mellon University
,
Program Chairs:
Andrew McCallum
University of Massachusetts Amherst
,
Sam Roweis
University of Toronto and Google

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Pascal
University of Helsinki
Xerox
Federation of Finnish Learned Societies
Google Inc.
NSF
Machine Learning Journal/Springer
Microsoft Research: Microsoft Research
Intel: Intel
Yahoo!
Helsinki Institute for Information Technology
IBM: IBM

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 July 2008

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Sixth Framework Programme

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ICML '08

Sponsor:

Microsoft Research
Intel
IBM

ICML '08: The 25th Annual International Conference on Machine Learning held in conjunction with the 2007 International Conference on Inductive Logic Programming

July 5 - 9, 2008

Helsinki, Finland

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Overall Acceptance Rate 140 of 548 submissions, 26%

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Cited By

Menon RChakrabarti I(2023)Low complexity VLSI architecture for improved primal–dual support vector machine learning coreMicroprocessors and Microsystems10.1016/j.micpro.2023.10480698(104806)Online publication date: Apr-2023
https://doi.org/10.1016/j.micpro.2023.104806
Piccialli VSciandrone M(2022)Nonlinear optimization and support vector machinesAnnals of Operations Research10.1007/s10479-022-04655-x314:1(15-47)Online publication date: 14-Apr-2022
https://doi.org/10.1007/s10479-022-04655-x
Ju XYan ZWang T(2021)Overview of Optimization Algorithms for Large-scale Support Vector Machines2021 International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW53433.2021.00119(909-916)Online publication date: Dec-2021
https://doi.org/10.1109/ICDMW53433.2021.00119
Fogler KGuron GWind LKeenum IHession WKrometis LStrawn LPruden APonder M(2019)Microbiota and Antibiotic Resistome of Lettuce Leaves and Radishes Grown in Soils Receiving Manure-Based Amendments Derived From Antibiotic-Treated CowsFrontiers in Sustainable Food Systems10.3389/fsufs.2019.000223Online publication date: 10-Apr-2019
https://doi.org/10.3389/fsufs.2019.00022
Lu MQiao LFeng DLi DLu X(2019)Mini-batch cutting plane method for regularized risk minimizationFrontiers of Information Technology & Electronic Engineering10.1631/FITEE.180059620:11(1551-1563)Online publication date: 16-Dec-2019
https://doi.org/10.1631/FITEE.1800596
Chauhan VDahiya KSharma A(2019)Problem formulations and solvers in linear SVMArtificial Intelligence Review10.1007/s10462-018-9614-652:2(803-855)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s10462-018-9614-6
Piccialli VSciandrone M(2018)Nonlinear optimization and support vector machines4OR10.1007/s10288-018-0378-216:2(111-149)Online publication date: 23-May-2018
https://doi.org/10.1007/s10288-018-0378-2
Ju XTian Y(2018)A divide-and-conquer method for large scale ź-nonparallel support vector machinesNeural Computing and Applications10.1007/s00521-016-2574-329:9(497-509)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1007/s00521-016-2574-3
Dong YDu BZhang LZhang L(2017)Exploring Locally Adaptive Dimensionality Reduction for Hyperspectral Image Classification: A Maximum Margin Metric Learning AspectIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing10.1109/JSTARS.2016.258774710:3(1136-1150)Online publication date: Mar-2017
https://doi.org/10.1109/JSTARS.2016.2587747
(2017)LAM3LNeurocomputing10.1016/j.neucom.2016.12.008235:C(1-9)Online publication date: 26-Apr-2017
https://dl.acm.org/doi/10.1016/j.neucom.2016.12.008
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