Search Results for author: Kristiaan Pelckmans

Found 7 papers, 0 papers with code

Detecting Suspicious Events in Fast Information Flows

no code implementations7 Jan 2021 Kristiaan Pelckmans, Moustafa Aboushady, Andreas Brosemyr

We describe a computational feather-light and intuitive, yet provably efficient algorithm, named HALFADO.

Longitudinal Support Vector Machines for High Dimensional Time Series

no code implementations22 Feb 2020 Kristiaan Pelckmans, Hong-Li Zeng

We consider the problem of learning a classifier from observed functional data.

Time Series

APTER: Aggregated Prognosis Through Exponential Reweighting

no code implementations20 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.

FADO: A Deterministic Detection/Learning Algorithm

no code implementations7 Nov 2017 Kristiaan Pelckmans

This paper proposes and studies a detection technique for adversarial scenarios (dubbed deterministic detection).

Fault Detection Learning Theory

A machine-learning approach to measuring the escape of ionizing radiation from galaxies in the reionization epoch

no code implementations31 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

Sparse Estimation From Noisy Observations of an Overdetermined Linear System

no code implementations12 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.

A Risk Minimization Principle for a Class of Parzen Estimators

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.

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