Chord: A scalable peer-to-peer lookup service for internet applications
Proceedings of the 2001 conference on Applications, technologies, architectures, and protocols for computer communications
Looking up data in P2P systems
Communications of the ACM
Proceedings of the 13th annual ACM international conference on Multimedia
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
Distributed classification in peer-to-peer networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery 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
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
On classifying drifting concepts in P2P networks
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
P2PDocTagger: content management through automated P2P collaborative tagging
Proceedings of the VLDB Endowment
Classification in P2P networks with cascade support vector machines
ACM Transactions on Knowledge Discovery from Data (TKDD)
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Distributed classification aims to learn with accuracy comparable to that of centralized approaches but at far lesser communication and computation costs. By nature, P2P networks provide an excellent environment for performing a distributed classification task due to the high availability of shared resources, such as bandwidth, storage space, and rich computational power. However, learning in P2P networks is faced with many challenging issues; viz., scalability, peer dynamism, asynchronism and fault-tolerance. In this paper, we address these challenges by presenting CEMPaR--a communication-efficient framework based on cascading SVMs that exploits the characteristics of DHT-based lookup protocols. CEMPaR is designed to be robust to parameters such as the number of peers in the network, imbalanced data sizes and class distribution while incurring extremely low communication cost yet maintaining accuracy comparable to the best-in-the-class approaches. Feasibility and effectiveness of our approach are demonstrated with extensive experimental studies on real and synthetic datasets.