全称为再生核希尔伯特空间理论(reproducing kernel Hilbert space,RKHS,用H表示)。 如果一个学习问题的解满足: 存在, 唯一, 关于数据连续 则被认为是适定的。 在之前的学习算法定义下,一般的方法通过最小化经验误差 minf∈H1n∑i=1nV(f(xi),yi), 来寻找最优解,但这个优化问题往往都是病态的(不适定的,即自...
不过,毕竟这里GP的专栏,我们的主角当是GP中最为常见的kernel,这个桂冠当然是属于Squared exponential (SE) kernel的啦!当然它还有很多常用名,比如Radial Basis Function(RBF)kernel,还有Gaussian kernel! 或许你会问,为什么这个是最常用的呢? 因为它的别叫高斯核! 好吧,玩笑啦!其实当然是因为它的性质好啦! 仔细回...
kernel learning嘛，其中最为典型的要数Support Vector Machine (SVM), Gaussian Process (GP),还有Princ...
高斯过程作为预测领域的非参数估计方法，核心在于其预测能力。本文从预测角度出发，深入探讨高斯过程学习与预测的机制，着重于理解其背后的数学原理。首先，引入核函数概念，解释核函数在高斯过程中的作用，以及其与正则化方法之间的等价关系。通过核方法的引入，直观展示高斯过程学习的本质。为更好地理解高斯过...
蓦风星吟：【答疑解惑-II】——不满足正态分布的数据到底能不能用Gaussian process的方法呢？224 赞同...
[1] Chen, Zexun, Jun Fan, and Kuo Wang. "Remarks on multivariate Gaussian Process."arXiv ...
The equivalent kernel [1] is a way of understanding how Gaussian process regression works for large sample sizes based on a continuum limit. In this paper we show (1) how to approximate the equivalent kernel of the widely-used squared exponential (or Gaussian) kernel and related kernels, and...
This code constructs covariance kernel for the Gaussian process that is equivalent to infinitely wide, fully connected, deep neural networks. To use the code, runrun_experiments.py, which uses NNGP kernel to make full Bayesian prediction on the MNIST dataset. ...
However, no equivalent Gaussian process model for near constant acceleration has been formulated. We develop an equivalent Gaussian process kernel for NCAM to be used for time-series prediction. 展开 关键词: Bayesian methods Gaussian processes Kalman filter near constant acceleration model periodic ...
In the limit where the width of a network is taken to infinity (network is thus overparameterized), neural network training with a certain random initialization scheme can be described by ridgeless kernel regression with the Neural Network Gaussian Process kernel (NNGPK) if only the last layer ...