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Space-time kernels

Wang, J; CHENG, T; Haworth, J; (2012) Space-time kernels. In: Shi, W and Goodchild, M and Lees, B, (eds.) Advances in geospatial information science. CRC Press: Leiden, NL.

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Kernel methods are a class of algorithms for pattern recognition. They play an important role in the current research area of spatial and temporal analysis since they are theoretically well-founded methods that show good performance in practice. Over the years, kernel methods have been applied to various fields including machine learning, statistical analysis, imaging processing, text categorization, handwriting recognition and many others. More recently, kernel-based methods have been introduced to spatial analysis and temporal analysis. However, how to define kernels for space-time analysis is still not clear. In the paper, we firstly review the relevant kernels for spatial and temporal analysis, then a space-time kernel function (STK) is presented based on the principle of convolution kernel for space-time analysis. Furthermore, the proposed space-time kernel function (STK) is applied to model space-time series using support vector regression algorithm. A case study is presented in which STK is used to predict China’s annual average temperature. Experimental results reveal that the space-time kernel is an effective method for space-time analysis and modelling

Type:Book chapter
Title:Space-time kernels
UCL classification:UCL > School of BEAMS > Faculty of Engineering Science > Civil, Environmental and Geomatic Engineering

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