Developing machine intelligence within p2p networks using a distributed associative memory

Abstract

In this paper, we discuss machine intelligence for conducting routine tasks within the Internet. We demonstrate a technique, called the Distributed Associative Memory Tree (DASMET), to deal with multi-feature recognition in a peer-to-peer (P2P)-based system. Shared content in P2P-based system is predominantly multimedia files. Multi-feature is an appealing way to tackle pattern recognition in this domain. In our scheme, the information held at individual peers is integrated into a common knowledge base in a logical tree like structure and relies on the robustness of a well-designed structured P2P overlay to cope with dynamic networks. Additionally, we also incorporate a consistent and secure backup scheme to ensure its reliability. We compare our scheme to the Backpropagation network and the Radial Basis Function (RBF) network on two standard datasets, for comparative accuracy. We also show that our scheme is scalable as increasing the number of stored patterns does not significantly affect the processing time. © 2013 Springer-Verlag Berlin Heidelberg.