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检索条件"机构=Parallel and Distributed Processing Laboratory Department of Applied Informatics"

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distributed privacy-preserving P2P data mining via Probabilistic Neural Network Committee Machines

Distributed privacy-preserving P2P data mining via Probabili...

引用

International Conference on Information, Intelligence, Systems and Applications

作者： Yiannis Kokkinos Konstantinos Margaritis Parallel and Distributed Processing Laboratory Department of Applied Informatics University of Macedonia

ISBN: (纸本)9781479907694

This work describes a probabilistic neural network (PNN) committee machine for Peer-to-Peer data mining. The pattern neurons of the PNN committee are composed of locally trained class-specialized regularization network Peer classifiers. The training takes into account the asynchronous distributed and privacy-preserving requirements that can be met in P2P systems. The Peer classifiers are first trained in parallel based on their local data. While no local data exchange is possible among them, the peers can exchange their classifiers in the form of binaries, or agents. Then an asynchronous distributed computing P2P cycle is executed to construct a mutual validation matrix. The train set of one Peer becomes the validation set of the other and only average rates are returned back. From this matrix we demonstrate that it is possible to perform weight based ensemble selection of best peer members for every class and in this way to find class-specialized Peer modules for the committee machine.

关键词： Neural Networks Peer-to-Peer data mining Privacy-preserving distributed computing Committee machines

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parallel implementation of the Wu-Manber algorithm using the OpenCL framework

Parallel implementation of the Wu-Manber algorithm using the...

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8th International Workshop on Artificial Intelligence Applications and Innovations, AIAI 2012: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB

作者： Pyrgiotis, Themistoklis K. Kouzinopoulos, Charalampos S. Margaritis, Konstantinos G. Parallel and Distributed Processing Laboratory Department of Applied Informatics University of Macedonia 156 Egnatia str. 54006 Thessaloniki Greece

ISBN: (纸本)9783642334115

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 these processes have become inadequate, due to an increasing demand for more computational power. Graphic cards offer a high parallelism computational power improving the performance of applications. This paper evaluates the performance of the Wu-Manber algorithm implemented with the OpenCL framework, by presenting the running time of the experiments compared with the corresponding sequential time. © 2012 IFIP International Federation for Information processing.

关键词： Smart cards

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A regularization network committee machine of isolated regularization networks for distributed privacy preserving data mining

A regularization network committee machine of isolated regul...

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8th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2012

作者： Kokkinos, Yiannis Margaritis, Konstantinos G. Parallel and Distributed Processing Laboratory Department of Applied Informatics University of Macedonia 156 Egnatia str. 54006 Thessaloniki Greece

ISBN: (纸本)9783642334085

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 combiner committee in an embedded architecture. After the committee training no module will know anything else except its own local data. This privacy preserving obligation is a challenging problem for trainable combiners but crucial in real world applications. Only classifiers in the form of binaries or agents can be sent to others to validate their local data and sent back average classification rates. From this fully distributed and privacy preserving mutual validation a coarse-grained matrix can be formed to map all members. We demonstrate that it is possible to fully exploit this mutual validation matrix to efficiently train another regularization network as a meta learner combiner for the committee. © 2012 IFIP International Federation for Information processing.

关键词： Data mining

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Multithreaded implementation of the Slope One algorithm for collaborative filtering

Multithreaded implementation of the Slope One algorithm for ...

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8th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2012

作者： Karydi, Efthalia Margaritis, Konstantinos G. University of Macedonia Department of Applied Informatics Parallel and Distributed Processing Laboratory 156 Egnatia str. 54006 Thessaloniki Greece

ISBN: (纸本)9783642334085

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-based companies having considerable impact on their profits. This paper implements a parallel version of the collaborative filtering algorithm Slope One, which has advantages such as its efficiency and the ability to update data dynamically. The presented version is parallely implemented with the use of the OpenMP API and its performance is evaluated on a multi-core system. © 2012 IFIP International Federation for Information processing.

关键词： Recommender systems

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parallel implementation of the slope one algorithm for collaborative filtering

Parallel implementation of the slope one algorithm for colla...

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2012 16th Panhellenic Conference on informatics, PCI 2012

作者： Karydi, Efthalia Margaritis, Konstantinos G. Parallel and Distributed Processing Laboratory Department of Applied Informatics University of Macedonia 156 Egnatia str. 54006 Thessaloniki Greece

ISBN: (纸本)9780769548258

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-based companies having considerable impact on their profits. This paper implements two parallel versions of the collaborative filtering algorithm Slope One, which has advantages such as its efficiency and the ability to update data dynamically. The first presented version is parallely implemented with the use of the OpenMP API and its performance is evaluated on a multi-core system. The second is an hybrid approach using both OpenMP and MPI and its performance is evaluated in an homogeneous and an heterogeneous cluster. Experiments proved that the multithreaded version is 9,5 times faster than the sequential algorithm. © 2012 IEEE.

关键词： parallel programming

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parallelism, localization and chain gradient tuning combinations for fast scalable probabilistic neural networks in data mining applications

Parallelism, localization and chain gradient tuning combinat...

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7th Hellenic Conference on Artificial Intelligence, SETN 2012

作者： Kokkinos, Yiannis Margaritis, Konstantinos Parallel and Distributed Processing Laboratory Department of Applied Informatics University of Macedonia 156 Egnatia str. 54006 Thessaloniki Greece

ISBN: (纸本)9783642304477

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 studied here, namely data parallel, neuron parallel and pipelining. Localization methods via clustering algorithms are utilized to reduce the hidden layer size of PNNs. A problem of localization may be present in the case of multi-class data. In this paper we propose two simple fast approximate solutions. The first is using sigma smoothing parameters obtained from the parallel PNN initial training directly to clustering. In this case a substantial reduction of neurons is achieved without significant loss of recognition accuracy. The second is an effort for an additional tuning. Via confidence outputs we employ a chain training approach to tune for the best possible PNN architecture. © 2012 Springer-Verlag.

关键词： Neural networks

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A distributed asynchronous and privacy preserving neural network ensemble selection approach for peer-to-peer data mining 12

A distributed asynchronous and privacy preserving neural net...

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5th Balkan Conference in informatics, BCI 2012

作者： Kokkinos, Yiannis Margaritis, Konstantinos G. Department of Applied Informatics Parallel and Distributed Processing Laboratory University of Macedonia P.O. Box 1591 54006 Thessaloniki Greece

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.

关键词： Neural networks

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A parallel Radial Basis Probabilistic Neural Network for scalable data mining in distributed memory machines

A Parallel Radial Basis Probabilistic Neural Network for sca...

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International Conference on Tools with Artificial Intelligence

作者： Yiannis Kokkinos Konstantinos Margaritis Parallel and Distributed Processing Laboratory Department of Applied Informatics University of Macedonia

ISBN: (纸本)9781479902279

This work presents scalable algorithms for basic construction of parallel Radial Basis Probabilistic Neural Networks. The final goal is to build a neural network that can efficiently be implemented in distributed memory machines. Thus a fast simple parallel training scheme for RBPNNs is studied, that is based almost solely on Gaussian summations which can by their part be efficiently mapped on parallel as well as on pipeline distributed machines. The suggested training scheme is tested for accuracy and performance and can guarantee simplicity, parallelization and linear speed ups in common parallel implementations, namely neuron parallel and pipelining studied here.

关键词： parallel processing Radial Basis Probabilistic Neural networks Large Scale

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parallel Implementation of the Slope One Algorithm for Collaborative Filtering

Parallel Implementation of the Slope One Algorithm for Colla...

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Panhellenic Conference on informatics (PCI)

作者： Efthalia Karydi Konstantinos G. Margaritis Parallel and Distributed Processing Laboratory Department of Applied Informatics University of Macedonia Thessaloniki Greece

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-based companies having considerable impact on their profits. This paper implements two parallel versions of the collaborative filtering algorithm Slope One, which has advantages such as its efficiency and the ability to update data dynamically. The first presented version is parallely implemented with the use of the OpenMP API and its performance is evaluated on a multi-core system. The second is an hybrid approach using both OpenMP and MPI and its performance is evaluated in an homogeneous and an heterogeneous cluster. Experiments proved that the multithreaded version is 9,5 times faster than the sequential algorithm.

关键词： Prediction algorithms Collaboration Clustering algorithms Recommender systems Instruction sets Message systems Arrays

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A Performance evaluation of the preprocessing phase of multiple keyword matching algorithms

A Performance evaluation of the preprocessing phase of multi...

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15th Panhellenic Conference on informatics, PCI 2011

作者： Kouzinopoulos, Charalampos S. Margaritis, Konstantinos G. Parallel and Distributed Processing Laboratory Department of Applied Informatics University of Macedonia 156 Egnatia Str. 54006 Thessaloniki Greece

ISBN: (纸本)9780769543895

Multiple keyword matching is an important problem in text processing that involves the location of all the positions of an input string where one or more keywords from a finite set occur. Modern multiple keyword matching algorithms can scan the input string in a single pass by preprocessing the keyword set, an essential phase that affects the overall performance of each algorithm. This paper presents a performance evaluation in terms of preprocessing of the well known Commentz-Walter, Wu-Manber, Set Backward Oracle Matching and Salmela-Tarhio-KytÖjoki multiple keyword matching algorithms for different types of keywords and for several problem parameters. © 2011 IEEE.

关键词： Text processing

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