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高斯过程是一种非参数化的学习方法，它可以很自然的用于regression，也可以用于classification。本程序用高斯过程实现分类！-Gaussian process is a non - parametric method of learning, it is very natural for regression. can also be used for classification. The procedures used to achieve classification G
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Variational Bayesian Multinomial Probit Regression with
Gaussian Process Priors. -Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors.
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单输出高斯过程回归 代理模型 回归 可代替神经网络-Single output Gaussian process regression
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高斯过程回归及分类的代码，内容全，有实例，注释清晰。包括分类系列和预测回归系列，值得感兴趣的同学学习借鉴。-Gaussian process regression and classification code, content, there are instances, comments clear.Including classification and forecasting return series, is worthy of reference for anyone interest
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高斯过程回归，适用于初学高斯过程回归和分类的同学，有完整的实例与多种辅助函数说明,例如用高斯过程回归进行信道的盲均衡。对于高斯过程回归与分类的进一步学习有很大的帮助。-Gaussian process regression, suitable for beginners gaussian process regression and classification of the classmate, a complete instance and a variety of auxiliary fu
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