Learning Support Vector Machines from distributed data sources

Cornelia Caragea; Doina Caragea; Vasant Honavar

Learning Support Vector Machines from distributed data sources

Cornelia Caragea, Doina Caragea, Vasant Honavar

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this paper we address the problem of learning Support Vector Machine (SVM) classifiers from distributed data sources. We identify sufficient statistics for learning SVMs and present an algorithm that learns SVMs from distributed data by iteratively computing the set of sufficient statistics. We prove that our algorithm is exact with respect to its centralized counterpart and efficient in terms of time complexity.

Original language	English (US)
Title of host publication	Proceedings of the 20th National Conference on Artificial Intelligence and the 17th Innovative Applications of Artificial Intelligence Conference, AAAI-05/IAAI-05
Pages	1602-1603
Number of pages	2
Volume	4
State	Published - 2005
Event	20th National Conference on Artificial Intelligence and the 17th Innovative Applications of Artificial Intelligence Conference, AAAI-05/IAAI-05 - Pittsburgh, PA, United States Duration: Jul 9 2005 → Jul 13 2005

Other

Other	20th National Conference on Artificial Intelligence and the 17th Innovative Applications of Artificial Intelligence Conference, AAAI-05/IAAI-05
Country/Territory	United States
City	Pittsburgh, PA
Period	7/9/05 → 7/13/05

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@inproceedings{98a6b528cc7a4e77959f892521fd3580,

title = "Learning Support Vector Machines from distributed data sources",

abstract = "In this paper we address the problem of learning Support Vector Machine (SVM) classifiers from distributed data sources. We identify sufficient statistics for learning SVMs and present an algorithm that learns SVMs from distributed data by iteratively computing the set of sufficient statistics. We prove that our algorithm is exact with respect to its centralized counterpart and efficient in terms of time complexity.",

author = "Cornelia Caragea and Doina Caragea and Vasant Honavar",

year = "2005",

language = "English (US)",

volume = "4",

pages = "1602--1603",

booktitle = "Proceedings of the 20th National Conference on Artificial Intelligence and the 17th Innovative Applications of Artificial Intelligence Conference, AAAI-05/IAAI-05",

note = "20th National Conference on Artificial Intelligence and the 17th Innovative Applications of Artificial Intelligence Conference, AAAI-05/IAAI-05 ; Conference date: 09-07-2005 Through 13-07-2005",

}

Caragea, C, Caragea, D & Honavar, V 2005, Learning Support Vector Machines from distributed data sources. in Proceedings of the 20th National Conference on Artificial Intelligence and the 17th Innovative Applications of Artificial Intelligence Conference, AAAI-05/IAAI-05. vol. 4, pp. 1602-1603, 20th National Conference on Artificial Intelligence and the 17th Innovative Applications of Artificial Intelligence Conference, AAAI-05/IAAI-05, Pittsburgh, PA, United States, 7/9/05.

Learning Support Vector Machines from distributed data sources. / Caragea, Cornelia; Caragea, Doina; Honavar, Vasant.
Proceedings of the 20th National Conference on Artificial Intelligence and the 17th Innovative Applications of Artificial Intelligence Conference, AAAI-05/IAAI-05. Vol. 4 2005. p. 1602-1603.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Learning Support Vector Machines from distributed data sources

AU - Caragea, Cornelia

AU - Caragea, Doina

AU - Honavar, Vasant

PY - 2005

Y1 - 2005

N2 - In this paper we address the problem of learning Support Vector Machine (SVM) classifiers from distributed data sources. We identify sufficient statistics for learning SVMs and present an algorithm that learns SVMs from distributed data by iteratively computing the set of sufficient statistics. We prove that our algorithm is exact with respect to its centralized counterpart and efficient in terms of time complexity.

AB - In this paper we address the problem of learning Support Vector Machine (SVM) classifiers from distributed data sources. We identify sufficient statistics for learning SVMs and present an algorithm that learns SVMs from distributed data by iteratively computing the set of sufficient statistics. We prove that our algorithm is exact with respect to its centralized counterpart and efficient in terms of time complexity.

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M3 - Conference contribution

AN - SCOPUS:29344451262

VL - 4

SP - 1602

EP - 1603

BT - Proceedings of the 20th National Conference on Artificial Intelligence and the 17th Innovative Applications of Artificial Intelligence Conference, AAAI-05/IAAI-05

T2 - 20th National Conference on Artificial Intelligence and the 17th Innovative Applications of Artificial Intelligence Conference, AAAI-05/IAAI-05

Y2 - 9 July 2005 through 13 July 2005

ER -

Learning Support Vector Machines from distributed data sources

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