Search Results for author: Bart De Moor

Found 11 papers, 6 papers with code

Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks

no code implementations6 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 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.

Time Series Time Series Clustering

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.

Dynamic Time Warping Time Series

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.

Easy Hyperparameter Search Using Optunity

1 code implementation2 Dec 2014 Marc Claesen, Jaak Simm, Dusan Popovic, Yves Moreau, Bart De Moor

Optunity is a free software package dedicated to hyperparameter optimization.

Hyperparameter Optimization

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

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.

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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