| 发明名称 | Method and system of data modelling | ||
| 摘要 | A method for modelling a dataset includes a training phase, wherein the dataset is applied to a non-stationary Gaussian process kernel in order to optimize the values of a set of hyperparameters associated with the Gaussian process kernel, and an evaluation phase in which the dataset and Gaussian process kernel with optimized hyperparameters are used to generate model data. The evaluation phase includes a nearest neighbor selection step. The method may include generating a model at a selected resolution. | ||
| 申请公布号 | US8768659(B2) | 申请公布日期 | 2014.07.01 |
| 申请号 | US200913119952 | 申请日期 | 2009.09.18 |
| 申请人 | The University of Sydney | 发明人 | Vasudevan Shrihari;Ramos Fabio Tozeto;Nettleton Eric;Durrant-Whyte Hugh |
| 分类号 | G06F7/60 | 主分类号 | G06F7/60 |
| 代理机构 | Blakely, Sokoloff, Taylor & Zafman LLP | 代理人 | Blakely, Sokoloff, Taylor & Zafman LLP |
| 主权项 | 1. A method for modelling data based on a dataset, comprising: in a computing system, executing: a training phase, wherein the dataset is applied to a non-stationary Gaussian process kernel in order to optimize the values of a set of hyperparameters associated with the non-stationary Gaussian process kernel, and an evaluation phase in which the dataset and the non-stationary Gaussian process kernel with optimized hyperparameters are used to generate model data, wherein the evaluation phase comprises a nearest neighbor selection operation, and wherein only a selected subset of nearest neighbor data in the dataset is used to generate each corresponding model data. | ||
| 地址 | The University of Sydney, New South Wales AU | ||