Cascade RSVM in Peer-to-Peer Networks
Abstract
The goal of distributed learning in P2P networks is to achieve results as close as possible to those from centralized approaches. Learning models of classification in a P2P network faces several challenges like scalability, peer dynamism, asynchronism and data privacy preservation. In this paper, we study the feasibility of building SVM classifiers in a P2P network. We show how cascading SVM can be mapped to a P2P network of data propagation. Our proposed P2P SVM provides a method for constructing classifiers in P2P networks with classification accuracy comparable to centralized classifiers and better than other distributed classifiers. The proposed algorithm also satisfies the characteristics of P2P computing and has an upper bound on the communication overhead. Extensive experimental results confirm the feasibility and attractiveness of this approach.
Cite
Text
Ang et al. "Cascade RSVM in Peer-to-Peer Networks." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2008. doi:10.1007/978-3-540-87479-9_22Markdown
[Ang et al. "Cascade RSVM in Peer-to-Peer Networks." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2008.](https://mlanthology.org/ecmlpkdd/2008/ang2008ecmlpkdd-cascade/) doi:10.1007/978-3-540-87479-9_22BibTeX
@inproceedings{ang2008ecmlpkdd-cascade,
title = {{Cascade RSVM in Peer-to-Peer Networks}},
author = {Ang, Hock Hee and Gopalkrishnan, Vivekanand and Hoi, Steven C. H. and Ng, Wee Keong},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2008},
pages = {55-70},
doi = {10.1007/978-3-540-87479-9_22},
url = {https://mlanthology.org/ecmlpkdd/2008/ang2008ecmlpkdd-cascade/}
}