Analysis and Theory of High-Dimension Space Geometry for Artificial Neural Networks

WANG Shou-jue; WANG Bai-nan

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Analysis and Theory of High-Dimension Space Geometry for Artificial Neural Networks

更新时间：2025-07-16

- Analysis and Theory of High-Dimension Space Geometry for Artificial Neural Networks
- Acta Electronica Sinica Vol. 30, Issue 1, Pages: 1-4(2002)
- 作者机构：
  
  中国科学院半导体研究所神经网络实验室,北京,100083
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP183
- Published：2002
- 稿件说明：
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WANG Shou-jue, WANG Bai-nan. Analysis and Theory of High-Dimension Space Geometry for Artificial Neural Networks[J]. Acta Electronica Sinica, 2002, 30(1): 1-4.
DOI：

WANG Shou-jue, WANG Bai-nan. Analysis and Theory of High-Dimension Space Geometry for Artificial Neural Networks[J]. Acta Electronica Sinica, 2002, 30(1): 1-4. DOI：

摘要

本文系统地讨论了作为用于分析人工神经网络的一种方法即多维空间几何方法

并对多维空间几何学进行系统的研究

推导了必要的定理

为人工神经网络的分析提供了新的手段.

Abstract

We systematically discuss a high-dimensional space geometry method used for analyzing artificial neural networks

do some research work about the high-dimensional space geometry and prove the necessary theorems.It is a novel tool to analyze artificial neural networks.

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