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Functional classification in Hilbert spaces

机译:希尔伯特空间中的功能分类

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Let X be a random variable taking values in a separable Hilbert space X, with label Y/spl isin/{0,1}. We establish universal weak consistency of a nearest neighbor-type classifier based on n independent copies (X/sub i/,Y/sub i/) of the pair (X,Y), extending the classical result of Stone to infinite-dimensional Hilbert spaces. Under a mild condition on the distribution of X, we also prove strong consistency. We reduce the infinite dimension of X by considering only the first d coefficients of a Fourier series expansion of each X/sub i/, and then we perform k-nearest neighbor classification in /spl Ropf//sup d/. Both the dimension and the number of neighbors are automatically selected from the data using a simple data-splitting device. An application of this technique to a signal discrimination problem involving speech recordings is presented.
机译:令X为一个随机变量,该变量在可分希尔伯特空间X中采用值,标签为Y / spl isin / {0,1}。我们基于(X,Y)对的n个独立副本(X / sub i /,Y / sub i /)建立最近邻居类型分类器的通用弱一致性,将Stone的经典结果扩展到无穷维希尔伯特空格。在温和的条件下,我们还证明了X的强一致性。我们通过仅考虑每个X / sub i /的傅立叶级数展开的前d个系数来减小X的无限维,然后在/ spl Ropf // sup d /中执行k最近邻分类。使用简单的数据拆分设备,可以从数据中自动选择维数和邻居数。提出了该技术在涉及语音记录的信号辨别问题中的应用。

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