Castro José, Secretan Jimmy, Georgiopoulos Michael, DeMara Ronald, Anagnostopoulos Georgios, Gonzalez Avelino
Department of Computer Engineering, Technological Institute of Costa Rica, Cartago, Costa Rica.
Neural Netw. 2007 Jan;20(1):109-28. doi: 10.1016/j.neunet.2006.10.003. Epub 2006 Dec 4.
Fuzzy ARTMAP neural networks have been proven to be good classifiers on a variety of classification problems. However, the time that Fuzzy ARTMAP takes to converge to a solution increases rapidly as the number of patterns used for training is increased. In this paper we examine the time Fuzzy ARTMAP takes to converge to a solution and we propose a coarse grain parallelization technique, based on a pipeline approach, to speed-up the training process. In particular, we have parallelized Fuzzy ARTMAP without the match-tracking mechanism. We provide a series of theorems and associated proofs that show the characteristics of Fuzzy ARTMAP's, without matchtracking, parallel implementation. Results run on a BEOWULF cluster with three large databases show linear speedup as a function of the number of processors used in the pipeline. The databases used for our experiments are the Forrest CoverType database from the UCI Machine Learning repository and two artificial databases, where the data generated were 16-dimensional Gaussian distributed data belonging to two distinct classes, with different amounts of overlap (5% and 15%).
模糊ARTMAP神经网络已被证明在各种分类问题上都是优秀的分类器。然而，随着用于训练的模式数量增加，模糊ARTMAP收敛到一个解所需的时间会迅速增加。在本文中，我们研究了模糊ARTMAP收敛到一个解所需的时间，并提出了一种基于流水线方法的粗粒度并行化技术，以加速训练过程。特别是，我们在没有匹配跟踪机制的情况下对模糊ARTMAP进行了并行化。我们提供了一系列定理及相关证明，展示了没有匹配跟踪的模糊ARTMAP并行实现的特性。在具有三个大型数据库的BEOWULF集群上运行的结果表明，作为流水线中使用的处理器数量的函数，实现了线性加速。我们实验中使用的数据库是来自UCI机器学习库的森林覆盖类型数据库以及两个人工数据库，其中生成的数据是属于两个不同类别的16维高斯分布数据，具有不同程度的重叠（5%和15%）。
