Efficient Domain Decomposition for a Neural Network Learning Algorithm, used for the Dose Evaluation in External Radiotherapy - Archive ouverte HAL Access content directly
Book Sections Year : 2010

Efficient Domain Decomposition for a Neural Network Learning Algorithm, used for the Dose Evaluation in External Radiotherapy

Abstract

The purpose of this work is to further study the relevance of accelerating the Monte Carlo calculations for the gamma rays external radiotherapy through feed-forward neural networks. We have previously presented a parallel incremental algorithm that builds neural networks of reduced size, while providing high quality approximations of the dose deposit. Our parallel algorithm consists in a regular decomposition of the initial learning dataset (also called learning domain) in as much subsets as available processors. However, the initial learning set presents heterogeneous signal complexities and consequently, the learning times of regular subsets are very different. This paper presents an efficient learning domain decomposition which balances the signal complexities across the processors. As will be shown, the resulting irregular decomposition allows for important gains in learning time of the global network.

Dates and versions

hal-00520154 , version 1 (22-09-2010)

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Marc Sauget, Remy Laurent, Julien Henriet, Michel Salomon, Regine Gschwind, et al.. Efficient Domain Decomposition for a Neural Network Learning Algorithm, used for the Dose Evaluation in External Radiotherapy. Konstantinos Diamantaras and Wlodek Duch and Lazaros Iliadis. Artificial Neural Networks - ICANN 2010 20th International Conference Proceedings, Springer-Heidelberg, pp.261-266, 2010, Lecture Notes in Computer Science, Volume 6352, ⟨10.1007/978-3-642-15819-3_34⟩. ⟨hal-00520154⟩
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