公开(公告)号：US06269323B1

公开(公告)日：2001-07-31

申请号：US08846039

申请日：1997-04-25

申请人： Vladimir Naumovich Vapnik ,  Steven Eugene Golowich

发明人： Vladimir Naumovich Vapnik ,  Steven Eugene Golowich

IPC分类号： G06E100

CPC分类号： G06F17/17

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.

摘要翻译： 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际功能的方法。 通过对所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扩展的功能来估计该功能。 通过使用定义为内核函数扩展的近似来实现术语数量的减少，内核函数在希尔伯特空间中形成内积。 通过查找测量的支持向量，可以指定扩展功能。 根据本发明的估计中的术语数量通常远小于正在估计的现实世界现象的观察次数。 在一个实施例中，功能估计方法可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。