Gaussian Radial Basis Function (RBF) Kernels are the most-often-employed
kernels in artificial intelligence and machine learning routines for providing
optimally-best results in contrast to their respective counter-parts. However,
a little is known about the application of the Generalized Gaussian Radial
Basis Function on various machine learning algorithms namely, kernel
regression, support vector machine (SVM) and pattern-recognition via neural
networks. The results that are yielded by Generalized Gaussian RBF in the
kernel sense outperforms in stark contrast to Gaussian RBF Kernel, Sigmoid
Function and ReLU Function. This manuscript demonstrates the application of the
Generalized Gaussian RBF in the kernel sense on the aforementioned machine
learning routines along with the comparisons against the aforementioned
functions as well.

应用广义高斯径向基函数（RBF）在机器学习算法中的性能优于其他函数，通过对比高斯 RBF 核函数、Sigmoid 函数和 ReLU 函数，本文展示了广义高斯 RBF 在核函数中的应用及其在核回归、支持向量机（SVM）和模式识别中的成果。

由广义高斯 RBF 生成的复制核希尔伯特空间的预约 $L^2$- 度量

An appointment with Reproducing Kernel Hilbert Space generated by  Generalized Gaussian RBF as $L^2-$measure

Deep neural networks (DNNs) have recently been achieving state-of-the-art
performance on a variety of pattern-recognition tasks, most notably visual
classification problems. Given that DNNs are now able to classify objects in
images with near-human-level performance, questions naturally arise as to what
differences remain between computer and human vision. A recent study revealed
that changing an image (e.g. of a lion) in a way imperceptible to humans can
cause a DNN to label the image as something else entirely (e.g. mislabeling a
lion a library). Here we show a related result: it is easy to produce images
that are completely unrecognizable to humans, but that state-of-the-art DNNs
believe to be recognizable objects with 99.99% confidence (e.g. labeling with
certainty that white noise static is a lion). Specifically, we take
convolutional neural networks trained to perform well on either the ImageNet or
MNIST datasets and then find images with evolutionary algorithms or gradient
ascent that DNNs label with high confidence as belonging to each dataset class.
It is possible to produce images totally unrecognizable to human eyes that DNNs
believe with near certainty are familiar objects, which we call "fooling
images" (more generally, fooling examples). Our results shed light on
interesting differences between human vision and current DNNs, and raise
questions about the generality of DNN computer vision.

本研究研究了深度神经网络在计算机视觉领域上的发展，并提出了 “欺骗图像” 的概念，在模型认为是熟悉对象的情况下生成了一系列对人类难以识别的图像，揭示了计算机视觉与人类视觉之间的有趣差异。