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    • 3. 发明授权
    • Method and system of data modelling
    • 数据建模方法与系统
    • US08849622B2
    • 2014-09-30
    • US12650444
    • 2009-12-30
    • Arman MelkumyanFabio Tozeto Ramos
    • Arman MelkumyanFabio Tozeto Ramos
    • G06F17/10G06F7/60G06F17/18
    • G06F17/18
    • A system for large scale data modelling is described. The system includes at least one data measurement sensor (230) for generating measured data, a training processor (240) to determine optimized hyperparameter values in relation to a Gaussian process covariance function including a sparse covariance function that is smooth and diminishes to zero outside of a characteristic hyperparameter length. An evaluation processor (260) determines model data from the Gaussian process covariance function with optimised hyperparameter values and measured data. Also described is methods for modelling date, including a method using a Gaussian process including a sparse covariance function that diminishes to zero outside of a characteristic length, wherein the characteristic length is determined from the data to be modelled.
    • 描述了一种用于大规模数据建模的系统。 该系统包括用于产生测量数据的至少一个数据测量传感器(230),训练处理器(240),用于确定与高斯过程协方差函数相关的优化超参数值,所述高斯过程协方差函数包括平滑的稀疏协方差函数,并且在 特征超参数长度。 评估处理器(260)从具有优化的超参数值和测量数据的高斯过程协方差函数确定模型数据。 还描述了用于建模日期的方法,包括使用包括在特征长度之外减小到零的稀疏协方差函数的高斯过程的方法,其中根据要建模的数据确定特征长度。