对于向量和超复杂值的神经网络的通用近似定理
Marcos Eduardo Valle1, Wington L Vital2, Guilherme Vieira1
1Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil.
概括
全球近似定理现在适用于更广泛的向量值神经网络类. 这种基于非退化的代数的扩展扩大了神经网络理论的适用性.
科学领域:
- 数学 数学 是一个数学.
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 全球近似定理是神经网络理论的基础.
- 现有的定理涵盖了实值的神经网络和一些超复杂值的神经网络.
- 超复杂值的神经网络是具有特定代数属性的向量值网络.
研究的目的:
- 为了扩展通用近似定理.
- 为了涵盖更广泛的矢量值神经网络.
- 将超复杂值的神经网络纳入具体案例.
主要方法:
- 介绍了非退化代数的概念.
- 对在非退化代数上定义的神经网络的通用近似定理的制定.
主要成果:
- 普遍近似定理是对定义在非退化代数上的神经网络进行概括的.
- 这种概括包括超复杂值的神经网络.
结论:
- 扩展定理扩大了各种矢量值神经网络的理论基础.
- 这项工作增强了对神经网络在各种数学和计算领域的理解和应用.
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