no code implementations • 7 Jan 2021 • Kristiaan Pelckmans, Moustafa Aboushady, Andreas Brosemyr
We describe a computational feather-light and intuitive, yet provably efficient algorithm, named HALFADO.
no code implementations • 22 Feb 2020 • Kristiaan Pelckmans, Hong-Li Zeng
We consider the problem of learning a classifier from observed functional data.
no code implementations • 20 Feb 2020 • Kristiaan Pelckmans, Liu Yang
This paper considers the task of learning how to make a prognosis of a patient based on his/her micro-array expression levels.
no code implementations • 7 Nov 2017 • Kristiaan Pelckmans
This paper proposes and studies a detection technique for adversarial scenarios (dubbed deterministic detection).
no code implementations • 31 Mar 2016 • Hannes Jensen, Erik Zackrisson, Kristiaan Pelckmans, Christian Binggeli, Kristiina Ausmees, Ulrika Lundholm
Recent observations of galaxies at $z \gtrsim 7$, along with the low value of the electron scattering optical depth measured by the Planck mission, make galaxies plausible as dominant sources of ionizing photons during the epoch of reionization.
Astrophysics of Galaxies
no code implementations • 12 Feb 2014 • Liang Dai, Kristiaan Pelckmans
This note studies a method for the efficient estimation of a finite number of unknown parameters from linear equations, which are perturbed by Gaussian noise.
no code implementations • NeurIPS 2007 • Kristiaan Pelckmans, Johan Suykens, Bart D. Moor
This paper explores the use of a Maximal Average Margin (MAM) optimality principle for the design of learning algorithms.