通过对流和卷积神经网络学习科罗博夫函数
Zhiying Fang1, Tong Mao2, Jun Fan3
1Institute of Applied Mathematics, Shenzhen Polytechnic University, Shenzhen, Guangdong, China fangzhiying@szpu.edu.cn.
Neural computation
|March 8, 2024
概括
本研究使用信息理论学习分析深层卷积神经网络 (CNN). 它为使用CNN进行强大的回归提供了一个理论框架,显示特定函数的收率.
科学领域:
- 机器学习 机器学习
- 深度学习理论 深度学习理论
- 信息理论 信息理论
背景情况:
- 将信息理论学习与深度学习相结合对于大数据挑战至关重要.
- 在深度学习模型中对卷积结构的理论理解仍然不完整.
- 强大的回归对于在机器学习中处理杂数据至关重要.
研究的目的:
- 使用学习理论开发深层卷积神经网络 (CNN) 算法的概括分析.
- 用currentropy诱导的损失函数来研究强大的回归.
- 为了弥合理解CNN的理论差距.
主要方法:
- 在深度学习中应用信息理论学习原则.
- 利用学习理论对CNN的概括分析.
- 专注于强大的回归与currentropy诱导的损失函数.
主要成果:
- 开发了基于深度CNN的强大的回归算法的显式收率.
- 在强大的回归中展示了CNN性能的理论基础.
- 当目标函数在科罗博夫空间内时,展示了收.
结论:
- 这项研究为强大的回归中的深度CNN提供了理论框架.
- 这些发现增强了对CNN的概括能力的理解.
- 这项研究提供了关于CNN算法的性能和局限性的见解.
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