Plagianakos, V.P. and Magoulas, George D. and Vrahatis, M.N. (2006) Distributed computing methodology for training neural networks in an image-guided diagnostic application. Computer Methods and Programs in Biomedicine 81 (3), pp. 228-235. ISSN 0169-2607.
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Distributed computing is a process through which a set of computers connected by a network is used collectively to solve a single problem. In this paper, we propose a distributed computing methodology for training neural networks for the detection of lesions in colonoscopy. Our approach is based on partitioning the training set across multiple processors using a parallel virtual machine. In this way, interconnected computers of varied architectures can be used for the distributed evaluation of the error function and gradient values, and, thus, training neural networks utilizing various learning methods. The proposed methodology has large granularity and low synchronization, and has been implemented and tested. Our results indicate that the parallel virtual machine implementation of the training algorithms developed leads to considerable speedup, especially when large network architectures and training sets are used.
|Additional Information:||Copyright © 2006 Elsevier Ireland Ltd.|
|Keyword(s) / Subject(s):||distributed computing, parallel implementations, parallel virtual machine—PVM, backpropagation training, image-guided diagnosis and surgery|
|School or Research Centre:||Birkbeck Schools and Research Centres > School of Business, Economics & Informatics > Computer Science and Informatics|
|Depositing User:||Sandra Plummer|
|Date Deposited:||11 Jun 2007|
|Last Modified:||17 Apr 2013 12:33|
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