arXiv:2606.23601stat.MLcs.LG2026-06被引 1
用线性回归视角解析神经网络，降低统计学家入门门槛。
Neural Networks as Linear Regression: An Introduction for Statisticians
- 将神经网络类比为线性回归，揭示其基础原理
- 通过常见定制化方法建立进阶学习基础
- 适合熟悉经典统计的学者快速理解神经网络
神经网络是计算机科学与统计学中常用的预测工具。然而，这一领域的入门门槛仍然较高，尤其对接受过频率学派训练的经典统计学家而言。本文通过描述能近似线性回归的神经网络，并阐述常见的可定制化设计，帮助读者理解其基本机制，为后续深入研究提供基础。
原文摘要 · Abstract (English)
Neural networks are a commonly used prediction tool in computer science and statistics. However, the barrier to entry of this interesting field remains high, particularly for classical statisticians trained in a frequentist perspective. In this letter, we demystify neural networks by describing networks that approximate a linear regression and describe common customizations that provide a foundation for further study.
神经网络统计学入门
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。