The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Are Virtualized Overlay Networks Too Much of a Good Thing?
IPTPS '01 Revised Papers from the First International Workshop on Peer-to-Peer Systems
Peer-to-peer information retrieval using self-organizing semantic overlay networks
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
The impact of DHT routing geometry on resilience and proximity
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
Making gnutella-like P2P systems scalable
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
Transport layer identification of P2P traffic
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Characterizing unstructured overlay topologies in modern P2P file-sharing systems
IMC '05 Proceedings of the 5th ACM SIGCOMM conference on Internet Measurement
Impact of neighbor selection on performance and resilience of structured p2p networks
IPTPS'05 Proceedings of the 4th international conference on Peer-to-Peer Systems
Real datasets for file-sharing peer-to-peer systems
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
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Self-reorganization offers the promise of improved performance, scalability and resilience in Peer-to-Peer (P2P) overlays. In practice however, the benefit of reorganization is often lower than the cost incurred in probing and adapting the overlay. We employ machine learning feature selection in a novel manner: to reduce communication cost thereby providing the basis of an efficient neighbor selection scheme for P2P overlays. In addition, our method enables nodes to locate and attach to peers that are likely to answer future queries with no a priori knowledge of the queries. We evaluate our neighbor classifier against live data from the Gnutella unstructured P2P network. We find Support Vector Machines with forward fitting predict suitable neighbors for future queries with over 90% accuracy while requiring minimal (