稀疏高维数据的插值
分析
这篇文章讨论了托马斯·卢克斯博士关于监督机器学习的几何视角的的研究，特别关注了为什么神经网络在图像识别等任务中表现出色。它强调了降维和选择性近似在神经网络中的重要性。文章还提到了基函数的位置以及高维数据中的采样现象。
引用 / 来源
查看原文"The insights from Thomas's work point at why neural networks are so good at problems which everything else fails at, like image recognition. The key is in their ability to ignore parts of the input space, do nonlinear dimension reduction, and concentrate their approximation power on important parts of the function."