Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
9-2008
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.
Discipline
Computer Sciences | Databases and Information Systems | OS and Networks
Publication
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I
Volume
5211
First Page
55
Last Page
70
ISBN
9783540874782
Identifier
10.1007/978-3-540-87479-9_22
Publisher
Springer Verlag
City or Country
Berlin
Citation
ANG, Hock Hee; Gopalkrishnan, Vivekanand; HOI, Steven C. H.; and NG, Wee Keong.
Cascade RSVM in Peer-to-Peer Network. (2008). Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I. 5211, 55-70.
Available at: https://ink.library.smu.edu.sg/sis_research/2383
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://dx.doi.org/10.1007/978-3-540-87479-9_22