Search Results for author: Bart De Moor

Found 16 papers, 9 papers with code

Length of Stay prediction for Hospital Management using Domain Adaptation

no code implementations29 Jun 2023 Lyse Naomi Wamba Momo, Nyalleng Moorosi, Elaine O. Nsoesie, Frank Rademakers, Bart De Moor

In this study, we predict early hospital LoS at the granular level of admission units by applying domain adaptation to leverage information learned from a potential source domain.

Domain Adaptation Length-of-Stay prediction +1

Duality in Multi-View Restricted Kernel Machines

2 code implementations26 May 2023 Sonny Achten, Arun Pandey, Hannes De Meulemeester, Bart De Moor, Johan A. K. Suykens

We propose a unifying setting that combines existing restricted kernel machine methods into a single primal-dual multi-view framework for kernel principal component analysis in both supervised and unsupervised settings.

Time Series

Client Recruitment for Federated Learning in ICU Length of Stay Prediction

1 code implementation28 Apr 2023 Vincent Scheltjens, Lyse Naomi Wamba Momo, Wouter Verbeke, Bart De Moor

In this work, we address the step prior to the initiation of a federated network for model training, client recruitment.

Federated Learning Length-of-Stay prediction

Multi-view Kernel PCA for Time series Forecasting

no code implementations24 Jan 2023 Arun Pandey, Hannes De Meulemeester, Bart De Moor, Johan A. K. Suykens

In this paper, we propose a kernel principal component analysis model for multi-variate time series forecasting, where the training and prediction schemes are derived from the multi-view formulation of Restricted Kernel Machines.

Time Series Time Series Forecasting

Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks

1 code implementation6 Apr 2021 Joachim Schreurs, Hannes De Meulemeester, Michaël Fanuel, Bart De Moor, Johan A. K. Suykens

A generative model may overlook underrepresented modes that are less frequent in the empirical data distribution.

The Bures Metric for Taming Mode Collapse in Generative Adversarial Networks

no code implementations28 Sep 2020 Hannes De Meulemeester, Joachim Schreurs, Michaël Fanuel, Bart De Moor, Johan Suykens

However, under certain circumstances, the training of GANs can lead to mode collapse or mode dropping, i. e. the generative models not being able to sample from the entire probability distribution.

The Bures Metric for Generative Adversarial Networks

no code implementations16 Jun 2020 Hannes De Meulemeester, Joachim Schreurs, Michaël Fanuel, Bart De Moor, Johan A. K. Suykens

However, under certain circumstances, the training of GANs can lead to mode collapse or mode dropping, i. e. the generative models not being able to sample from the entire probability distribution.

Applicability and interpretation of the deterministic weighted cepstral distance

1 code implementation8 Mar 2018 Oliver Lauwers, Bart De Moor

In this way, we provide a purely data-driven way to assess different underlying dynamics of input/output signal pairs, without the need for any system identification step.

BIG-bench Machine Learning Clustering +2

A time series distance measure for efficient clustering of input output signals by their underlying dynamics

no code implementations6 Mar 2017 Oliver Lauwers, Bart De Moor

The first class of methods employs a distance measure on time series (e. g. Euclidean, Dynamic Time Warping) and a clustering technique (e. g. k-means, k-medoids, hierarchical clustering) to find natural groups in the dataset.

Clustering Dynamic Time Warping +2

Building Classifiers to Predict the Start of Glucose-Lowering Pharmacotherapy Using Belgian Health Expenditure Data

no code implementations28 Apr 2015 Marc Claesen, Frank De Smet, Pieter Gillard, Chantal Mathieu, Bart De Moor

We present a novel risk profiling approach based exclusively on health expenditure data that is available to Belgian mutual health insurers.

Assessing binary classifiers using only positive and unlabeled data

2 code implementations26 Apr 2015 Marc Claesen, Jesse Davis, Frank De Smet, Bart De Moor

We provide theoretical bounds on the quality of our estimates, illustrate the importance of estimating the fraction of positives in the unlabeled set and demonstrate empirically that we are able to reliably estimate ROC and PR curves on real data.

Hyperparameter Search in Machine Learning

no code implementations7 Feb 2015 Marc Claesen, Bart De Moor

We introduce the hyperparameter search problem in the field of machine learning and discuss its main challenges from an optimization perspective.

BIG-bench Machine Learning

Fast Prediction with SVM Models Containing RBF Kernels

1 code implementation4 Mar 2014 Marc Claesen, Frank De Smet, Johan A. K. Suykens, Bart De Moor

We present an approximation scheme for support vector machine models that use an RBF kernel.

EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines

1 code implementation4 Mar 2014 Marc Claesen, Frank De Smet, Johan Suykens, Bart De Moor

EnsembleSVM is a free software package containing efficient routines to perform ensemble learning with support vector machine (SVM) base models.

Ensemble Learning

A Robust Ensemble Approach to Learn From Positive and Unlabeled Data Using SVM Base Models

1 code implementation13 Feb 2014 Marc Claesen, Frank De Smet, Johan A. K. Suykens, Bart De Moor

The included benchmark comprises three settings with increasing label noise: (i) fully supervised, (ii) PU learning and (iii) PU learning with false positives.

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