2 citations · 2 across the 10 of their papers we have counts for
13 papers
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…
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…
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…
Mixed Multiple Orthogonal Laurent Polynomials on the Unit Circle
Edmundo J. Huertas, Manuel Mañas
Mixed orthogonal Laurent polynomials on the unit circle of CMV type are constructed utilizing a matrix of moments and its Gauss--Borel factorization and employing a multiple extens…
On zero behavior of higher-order Sobolev-type discrete q-Hermite I orthogonal polynomials
Edmundo J. Huertas, Alberto Lastra, Anier Soria-Lorente +1
In this work, we investigate the sequence of monic q-Hermite I-Sobolev type orthogonal polynomials of higher-order, denoted as , which are orthog…