Application of Self Organizing Map to Preprocessing Input Vectors for Convolutional Neural Network

机译：自组织映射在卷积神经网络预处理输入向量中的应用

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Recently, the applications of Artificial Intelligence are widely spread in many areas of research. They almost use tailor made classification engine of Deep Learning, and many of such engines uses Convolutional Neural Networks. In this paper, we propose a method for preprocessing the un-structured data to the 2 dimensional data suitable for CNN using Self Organizing Map. The performance is evaluated with the experiments using KDD cup 99 data as input vectors.

机译：近来，人工智能的应用广泛地在许多研究领域中传播。他们几乎使用量身定制的深度学习分类引擎，其中许多引擎使用卷积神经网络。在本文中，我们提出了一种使用自组织映射将非结构化数据预处理为适用于CNN的二维数据的方法。使用KDD cup 99数据作为输入向量，通过实验评估了性能。

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《International Conference on Artificial Neural Networks》|2019年|96-100|共5页
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Hiroshi Dozono; Masafumi Tanaka;
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Self Organizing Map; Convolutional Neural Network; Data compression;

机译：自组织图;卷积神经网络资料压缩;

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4. Application of Self Organizing Map to Preprocessing Input Vectors for Convolutional Neural Network [C] . Hiroshi Dozono, Masafumi Tanaka International Conference on Artificial Neural Networks . 2019

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Application of Self Organizing Map to Preprocessing Input Vectors for Convolutional Neural Network

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