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Support vector method for function estimation

机译:支持向量函数估计

摘要

A method for estimating a real function that describes a phenomenon occurring in a space of any dimensionality is disclosed. The function is estimated by taking a series of measurements of the phenomenon being described and using those measurements to construct an expansion that has a manageable number of terms. A reduction in the number of terms is achieved by using an approximation that is defined as an expansion on kernel functions, the kernel functions forming an inner product in Hilbert space. By finding the support vectors for the measurements one specifies the expansion functions. The number of terms in an estimation according to the present invention is generally much less than the number of observations of the real world phenomenon that is being estimated. In one embodiment, the function estimation method may be used to reconstruct a radiation density image using Positron Emission Tomography (PET) scan measurements.
机译:公开了一种用于估计描述在任何维度的空间中发生的现象的实函数的方法。通过对描述的现象进行一系列测量并使用这些测量来构建具有可管理数量的项的展开来估计函数。通过使用定义为内核函数的展开的近似值,可以减少项的数量,这些内核函数在Hilbert空间中形成内积。通过找到测量的支持向量,可以指定扩展函数。根据本发明的估计中的项数通常比正在估计的现实世界现象的观测数少得多。在一个实施例中,功能估计方法可以用于使用正电子发射断层扫描(PET)扫描测量来重建辐射密度图像。

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