The nature of statistical learning theory
The nature of statistical learning theory
Boosting Algorithms for Parallel and Distributed Learning
Distributed and Parallel Databases - Special issue: Parallel and distributed data mining
Meta-Learning by Landmarking Various Learning Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Distributed Pasting of Small Votes
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Batch mode active learning and its application to medical image classification
ICML '06 Proceedings of the 23rd international conference on Machine learning
Learning the unified kernel machines for classification
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Distributed Data Mining in Peer-to-Peer Networks
IEEE Internet Computing
Agent-based Service-Oriented Intelligent P2P Networks for Distributed Classification
ICHIT '06 Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 02
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
An efficient update propagation algorithm for P2P systems
Computer Communications
Distributed classification in peer-to-peer networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Automatic document organization in a p2p environment
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
A study on reduced support vector machines
IEEE Transactions on Neural Networks
Communication-Efficient Classification in P2P Networks
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Collaborative classification over P2P networks
Proceedings of the 20th international conference companion on World wide web
Adaptive ensemble classification in p2p networks
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
Satrap: data and network heterogeneity aware P2P data-mining
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Distributed machine learning in networks by consensus
Neurocomputing
Classification in P2P networks with cascade support vector machines
ACM Transactions on Knowledge Discovery from Data (TKDD)
Graph diameter, eigenvalues, and minimum-time consensus
Automatica (Journal of IFAC)
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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.