Invariance of neighborhood relation under input space to feature space mapping
Pattern Recognition Letters
Agent-based Service-Oriented Intelligent P2P Networks for Distributed Classification
ICHIT '06 Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 02
Distributed classification in peer-to-peer networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Distributed Decision-Tree Induction in Peer-to-Peer Systems
Statistical Analysis and Data Mining
Cascade RSVM in Peer-to-Peer Networks
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
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
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Distributed classification aims to build an accurate classifier by learning from distributed data while reducing computation and communication cost A P2P network where numerous users come together to share resources like data content, bandwidth, storage space and CPU resources is an excellent platform for distributed classification However, two important aspects of the learning environment have often been overlooked by other works, viz., 1) location of the peers which results in variable communication cost and 2) heterogeneity of the peers' data which can help reduce redundant communication In this paper, we examine the properties of network and data heterogeneity and propose a simple yet efficient P2P classification approach that minimizes expensive inter-region communication while achieving good generalization performance Experimental results demonstrate the feasibility and effectiveness of the proposed solution.