Gaussian processes for machine learning-英文文献.pdf 9页内容提供方:wnqwwy20 大小:223.88 KB 字数:约2.47万字 发布时间:2017-06-16发布于浙江 浏览人气:262 下载次数:仅上传者可见 收藏次数:0 需要金币:*** 金币 (10金币=人民币1元)Gaussian processes for mach
我们使用高斯过程(GP,Gaussian process)来描述函数上的分布。形式上: 定义2.1 高斯过程(Gaussian process)是一组随机变量,其中任意有限个随机变量都具有联合高斯分布。 高斯过程(Gaussian process)完全由其均值函数和协方差函数确定。我们将实过程f\left( \mathbf{x}\right)的均值函数m\left( \mathbf{x}\right)和...
8. Technical docs are available at http://.gaussianprocess/gpml/code/matlab/doc/manual.pdf. 3012 GAUSSIAN PROCESSES FOR MACHINE LEARNING TOOLBOX 2. The GPML Toolbox We illustrate the modular structure of the GPML toolbox by means of a simple code example. ...
这些数据是从具有平方指数(SE)核且(ℓ,σf,σn)=(1,1,0.1)的高斯过程生成的。该图还显示了根据公式(2.24),使用这些超参数值得到的预测的2倍标准差误差线。注意,对于远离任何训练点的输入值,误差线是如何变大的。实际上,如果扩展x轴,我们会看到误差线反映出远离数据处过程的先验标准差σf。 如果我们将长...
Machine LearningGaussian Processes for Machine Learning by Carl E. Rasmussen, Christopher K. I. Williams Publisher: The MIT Press 2005ISBN/ASIN: 026218253XISBN-13: 9780262182539Number of pages: 266 Description:Gaussian processes (GPs) provide a principled, practical, probabilistic approach to ...
A Gaussian process (GP) is defined as a stochastic process with samples over time such that regardless of which finite linear combination is considered, the linear combination will have a joint Gaussian distribution. The value of GP models is that they provide a principled, practical, and probabi...
Gaussian process models are routinely used to solve hard machine learning problems. They are attractive because of their flexible non-parametric nature and computational simplicity. Treated within a Bayesian framework, very powerful statistical methods can be implemented which offer valid estimates of ...
Gaussian Processes for Machine Learning provides a principled, practical, probabilistic approach to learning using kernel machines. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied st
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