Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F09%3A00158805" target="_blank" >RIV/68407700:21230/09:00158805 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization

Original language description

We have developed an optimized cutting plane algorithm (OCA) for solving large-scale risk minimization problems. We prove that the number of iterations OCA requires to converge to a epsilon precise solution is approximately linear in the sample size. Wealso derive OCAS, an OCA-based linear binary SVM solver, and OCAM, a linear multi-class SVM solver. In an extensive empirical evaluation we show that OCAS outperforms current state-of-the-art SVM solvers like svmlight, svmperf and BMRM, achieving speedupfactor more than 1,200 over svmlight on some data sets and speedup factor of 29 over svmperf, while obtaining the same precise support vector solution.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

JD - Use of computers, robotics and its application

OECD FORD branch

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Project

<a href="/en/project/1M0567" target="_blank" >1M0567: Centre for Applied Cybernetics</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2009

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Machine Learning Research

ISSN

1532-4435

e-ISSN

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Volume of the periodical

10

Issue of the periodical within the volume

4

Country of publishing house

US - UNITED STATES

Number of pages

36

Pages from-to

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UT code for WoS article

000272346400001

EID of the result in the Scopus database

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