One of the most significant issues of the computational biology is the multiple pattern matching for locating nucleotides and amino acid sequence patterns into biological databases. Sequential implementations for thes...
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In this paper we consider large scale distributed committee machines where no local data exchange is possible between neural network modules. Regularization neural networks are used for both the modules as well as the...
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Recommender systems are mechanisms that filter information and predict a user's preference to an item. parallel implementations of recommender systems improve scalability issues and can be applied to internet-base...
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This work investigates the scalability of Probabilistic Neural Networks via parallelization and localization, and a chain gradient tuning. Since PNN model is inherently parallel three common parallel approaches are st...
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Context situation, which means a snapshot of the status of the real world, is formed by integrating a large amount of contexts collected from various resources. How to get the context situation and use the situation t...
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distributed social networks have emerged recently. Nevertheless, recommending friends in the distributed social networks has not been exploited fully. We propose FDist, a distributed common-friend estimation scheme th...
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distributed online social networks (DOSN) have emerged recently. Nevertheless, recommending friends in the distributed social networks has not been exploited fully. We propose BCE (Bloom Filter based Common-Friend Est...
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This work describes a fully asynchronous and privacy preserving ensemble selection approach for distributed data mining in peerto- peer applications. The algorithm builds a global ensemble model over large amounts of ...
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ISBN:
(纸本)9781450312400
This work describes a fully asynchronous and privacy preserving ensemble selection approach for distributed data mining in peerto- peer applications. The algorithm builds a global ensemble model over large amounts of data distributed over the peers in a network, without moving the data itself, and with little centralized coordination. Only classifiers are transmitted to other peers. Here the test set from one classifier is the train set of the other and vice versa. Regularization Networks are used as ensemble member classifiers. The approach constructs a mapping of all ensemble members to a mutual affinity matrix based on classification rates between them. After the mapping of all members the Affinity Propagation clustering algorithm is used for the selection phase. A classical asynchronous peer-to-peer cycle is continually executed for computing the mutual affinity matrix. The cycle composed of typical grid commands, like send local classifier to a peer k, check for received classifier m in the queue, compute local average positive hits, send results to peer m and send local classifier to a peer k+1. Thus the communication model used is simple point-to-point with send-receive commands to or from a single peer. The approach can also be implemented to other types of classifiers. Copyright 2012 ACM.
As the foundation of cloud computing, Server consolidation allows multiple computer infrastructures running as virtual machines in a single physical node. It improves the utilization of most kinds of resource but memo...
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Compensating CSP (cCSP) models long-running transactions. It can be used to specify service orchestrations written in programming languages like WS-BPEL. However, the original cCSP does not allow to model internal (no...
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