灰色机器学习简介
A brief introduction to the Grey Machine Learning
摘要：
简要介绍了基于核的灰色机器学习（GML）的关键技术。首先总结了灰色系统模型的一般形式，然后对灰色系统模型的非线性扩展进行了一般形式的研究。利用核隐式映射估计GML模型的非线性函数，通过扩展LSSVM的非参数形式，GML模型的非线性函数的估计也可以用核表示。本文还讨论了这种新框架相对于现有灰色模型和最小二乘支持向量机的优越性。最后，对该框架的未来发展方向进行了展望。
英文摘要：
This paper presents a brief introduction to the key points of the Grey Machine Learning (GML) based on the kernels. The general formulation of the grey system models have been firstly summarized, and then the nonlinear extension of the grey models have been developed also with general formulations. The kernel implicit mapping is used to estimate the nonlinear function of the GML model, by extending the nonparametric formulation of the LSSVM, the estimation of the nonlinear function of the GML model can also be expressed by the kernels. A short discussion on the priority of this new framework to the existing grey models and LSSVM have also been discussed in this paper. And the perspectives and future orientations of this framework have also been presented.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
