2 papers
cs.LG2026
Beyond the Gegenbauer Paradigm: q-Orthogonal Kernels for Machine Learning
Álvaro Sánchez-Paniagua Ríos, Juan P. Llerena, Alberto Lastra +2
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. Whi…
stat.ML2026
Structural interpretability in SVMs with truncated orthogonal polynomial kernels
Víctor Soto-Larrosa, Nuria Torrado, Edmundo J. Huertas
We study post-training interpretability for Support Vector Machines (SVMs) built from truncated orthogonal polynomial kernels. Since the associated reproducing kernel Hilbert space…