4 papers
Beyond the Gegenbauer Paradigm: q-Orthogonal Kernels for Machine Learning
Ãlvaro Sánchez-Paniagua RÃos, Álvaro Sánchez-Paniagua Ríos, Juan P. Llerena +3
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…
Structural interpretability in SVMs with truncated orthogonal polynomial kernels
VÃctor 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…
Dunkl derivative from moment differentiation
Edmundo J. Huertas, Alberto Lastra, Judit Minguez Ceniceros
The work analyzes the theory of Dunkl operator as a moment differential operator. This last operator generalizes the first one whenever the sequence of moments satisfies appropriat…
Symmetric Truncated Freud polynomials
Edmundo J. Huertas, Alberto Lastra, Francisco Marcellán +1
We define the family of symmetric truncated Freud polynomials , orthogonal with respect to the linear functional defined by \begin{equation*} \langle \mathbf…