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Reproducing Kernel Hilbert Space, Representer Theorem

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Part of the Mathematics in Industry book series (MATHINDUSTRY,volume 37)

Abstract

One of the key concepts in machine learning is the feature space, which is often referred to as the latent space. A feature space is usually a higher or lower-dimensional space than the original one where the input data lie (which is often referred to as the ambient space).

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  • DOI: 10.1007/978-981-16-6046-7_4
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References

  1. B. Schölkopf, A. J. Smola, F. Bach et al., Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press, 2002.

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  2. B. Schölkopf, R. Herbrich, and A. J. Smola, “A generalized representer theorem,” in International conference on computational learning theory. Springer, 2001, pp. 416–426.

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  3. G. Salton and M. McGill, Introduction to Modern Information Retrieval. McGraw Hill Book Company, 1983.

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Ye, J.C. (2022). Reproducing Kernel Hilbert Space, Representer Theorem. In: Geometry of Deep Learning. Mathematics in Industry, vol 37. Springer, Singapore. https://doi.org/10.1007/978-981-16-6046-7_4

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