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arXiv Machine Learning By \'Alvaro S\'anchez-Paniagua R\'ios, Juan P. Llerena, Alberto Lastra, Nuria Torrado, Edmundo J. Huertas

Beyond the Gegenbauer Paradigm: q-Orthogonal Kernels for Machine Learning

Read the original on arXiv Machine Learning →

arXiv:2608. 03482v1 Announce Type: new Abstract: The performance of Support Vector Machines (SVMs) critically depends on the kernel function choice, which enables implicit mapping of data into high-dimensional feature spaces.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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