Neural Network for Big Data Sets

V. Phu, Vo Thi Ngoc Tran
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引用次数: 0

Abstract

Machine learning (ML), neural network (NN), evolutionary algorithm (EA), fuzzy systems (FSs), as well as computer science have been very famous and very significant for many years. They have been applied to many different areas. They have contributed much to developments of many large-scale corporations, massive organizations, etc. Lots of information and massive data sets (MDSs) have been generated from these big corporations, organizations, etc. These big data sets (BDSs) have been the challenges of many commercial applications, researches, etc. Therefore, there have been many algorithms of the ML, the NN, the EA, the FSs, as well as computer science which have been developed to handle these massive data sets successfully. To support for this process, the authors have displayed all the possible algorithms of the NN for the large-scale data sets (LSDSs) successfully in this chapter. Finally, they have presented a novel model of the NN for the BDS in a sequential environment (SE) and a distributed network environment (DNE).
大数据集的神经网络
机器学习(ML),神经网络(NN),进化算法(EA),模糊系统(fs),以及计算机科学已经非常著名和非常重要了很多年。它们已被应用于许多不同的领域。他们为许多大型公司、大型组织等的发展做出了巨大贡献。这些大公司、组织等产生了大量的信息和海量数据集(mds)。这些大数据集(bds)已经成为许多商业应用、研究等方面的挑战。因此,已经有许多ML、NN、EA、FSs以及计算机科学的算法被开发出来,以成功地处理这些大量数据集。为了支持这一过程,作者在本章中成功地展示了针对大规模数据集(LSDSs)的所有可能的神经网络算法。最后,他们提出了一种新的连续环境(SE)和分布式网络环境(DNE)下的北斗系统神经网络模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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