Gibbs M N, MacKay D C
Cavendish Laboratory, Cambridge CB3 0HE, UK.
IEEE Trans Neural Netw. 2000;11(6):1458-64. doi: 10.1109/72.883477.
Gaussian processes are a promising nonlinear regression tool, but it is not straightforward to solve classification problems with them. In this paper the variational methods of Jaakkola and Jordan are applied to Gaussian processes to produce an efficient Bayesian binary classifier.
高斯过程是一种很有前景的非线性回归工具，但用它们来解决分类问题并非易事。在本文中，将亚科拉和乔丹的变分方法应用于高斯过程，以构建一个高效的贝叶斯二元分类器。
