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- W1736337583 abstract "There has been growing recent interest in probabilistic interpretations of kernel-based methods as well as learning in Banach spaces. The absence of a useful Lebesgue measure on an infinite-dimensional reproducing kernel Hilbert space is a serious obstacle for such stochastic models. We propose an estimation model for the ridge regression problem within the framework of abstract Wiener spaces and show how the support vector machine solution to such problems can be interpreted in terms of the Gaussian Radon transform." @default.
- W1736337583 created "2016-06-24" @default.
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- W1736337583 date "2013-10-17" @default.
- W1736337583 modified "2023-09-27" @default.
- W1736337583 title "The Gaussian Radon Transform and Machine Learning" @default.
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