Representative datasets for neural networks

Q2 Mathematics Electronic Notes in Discrete Mathematics Pub Date : 2018-07-01 DOI:10.1016/j.endm.2018.06.016

Rocio Gonzalez-Diaz , Eduardo Paluzo-Hidalgo, Miguel A. Gutiérrez-Naranjo

引用次数: 5

Abstract

Neural networks present big popularity and success in many fields. The large training time process problem is a very important task nowadays. In this paper, a new approach to get over this issue based on reducing dataset size is proposed. Two algorithms covering two different shape notions are shown and experimental results are given.

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神经网络的代表性数据集

神经网络在许多领域都很受欢迎并取得了成功。大训练时间过程问题是当今一个非常重要的课题。本文提出了一种基于减少数据集大小的新方法来解决这一问题。给出了两种不同形状概念的算法，并给出了实验结果。

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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来源期刊

Electronic Notes in Discrete Mathematics Mathematics-Discrete Mathematics and Combinatorics

CiteScore

1.30

自引率

0.00%

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