ML Anthology

Learning Support Vector Machines from Distributed Data Sources

Cornelia Caragea, Doina Caragea, Vasant G. Honavar
AAAI 2005 pp. 1602-1603
/aaai/2005/caragea2005aaai-learning/

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.

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Cite

Text

Caragea et al. "Learning Support Vector Machines from Distributed Data Sources." AAAI Conference on Artificial Intelligence, 2005.

Markdown

[Caragea et al. "Learning Support Vector Machines from Distributed Data Sources." AAAI Conference on Artificial Intelligence, 2005.](https://mlanthology.org/aaai/2005/caragea2005aaai-learning/)

BibTeX

@inproceedings{caragea2005aaai-learning,
  title     = {{Learning Support Vector Machines from Distributed Data Sources}},
  author    = {Caragea, Cornelia and Caragea, Doina and Honavar, Vasant G.},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2005},
  pages     = {1602-1603},
  url       = {https://mlanthology.org/aaai/2005/caragea2005aaai-learning/}
}