arXiv Open Access 2013

The Gaussian Radon Transform and Machine Learning

Irina Holmes Ambar Sengupta

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Abstrak

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.

Topik & Kata Kunci

stat.ML math.FA

Penulis (2)

Irina Holmes

Ambar Sengupta

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APA MLA BibTeX

Holmes, I., Sengupta, A. (2013). The Gaussian Radon Transform and Machine Learning. https://arxiv.org/abs/1310.4794

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Informasi Jurnal

Tahun Terbit: 2013
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓