Parallel Levenberg-Marquardt-Based Neural Network Training on Linux Clusters

Linux 集群上基于并行 Levenberg-Marquardt 的神经网络训练 - 案例研究

DOI：

发表时间：

2002

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Indian Conference on Computer Vision, Graphics & Image Processing

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作者：

N. R. Suri;Dipti Deodhare;P. Nagabhushan

本文讨论了模式分类问题使用神经网络。应用神经网络分类器用于对大量高维数据进行分类，这是一项艰巨的任务，因为训练过程是计算性的，贵了已知列车的并行实施方式- 研究范式为解决这一问题提供了一个可行的方案。通过利用Levenberg-Marquardt算法的大规模并行结构，在Linux机群上，利用LAM（Local Area Multi-computer）MPI实现了一种神经网络训练算法（消息传递接口）。实施，此外促进最大化计算的主要目标，加速，也是可移植的和可扩展的。一个标准的长凳- 一种用于神经网络训练的标记，大量卫星图像数据已被用于介绍并讨论实现的属性

This paper addresses the problem of pattern classification using neural networks. Applying neural network classifiers for classifying a large volume of high dimensional data is a difficult task as the training process is computationally expensive. A parallel implementation of the known train- ing paradigms offers a feasible solution to the problem. By exploiting the massively parallel structure of the Levenberg-Marquardt algorithm for non-linear optimization a training algorithm for neural networks has been implemented on a Linux cluster using LAM (Local Area Multi-computer) MPI (Message Passing Interface). The implementation, besides facilitating the main objective of maximising computational speedup, is also portable and scalable. A standard bench- mark for neural network training comprising a sufficiently large volume of satellite image data has been utilized to present and discuss the properties of the implementation

Parallel Levenberg-Marquardt-Based Neural Network Training on Linux Clusters - A Case Study

Linux 集群上基于并行 Levenberg-Marquardt 的神经网络训练 - 案例研究

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