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See detailClassification performance resulting from of 2-means
Ruwet, Christel ULg; Haesbroeck, Gentiane ULg

in Journal of Statistical Planning & Inference (2013), 143(2), 408-418

The k-means procedure is probably one of the most common nonhierachical clustering techniques. From a theoretical point of view, it is related to the search for the k principal points of the underlying ... [more ▼]

The k-means procedure is probably one of the most common nonhierachical clustering techniques. From a theoretical point of view, it is related to the search for the k principal points of the underlying distribution. In this paper, the classification resulting from that procedure for k=2 is shown to be optimal under a balanced mixture of two spherically symmetric and homoscedastic distributions. Then, the classification efficiency of the 2-means rule is assessed using the second order influence function and compared to the classification efficiencies of the Fisher and logistic discriminations. Influence functions are also considered here to compare the robustness to infinitesimal contamination of the 2-means method w.r.t. the generalized 2-means technique. [less ▲]

Detailed reference viewed: 72 (14 ULg)
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See detailClassification trees based on infrared spectroscopic data to discriminate between genuine and counterfeit medecines
Deconinck, Eric; Sacré, Pierre-Yves ULg; De Beer, Jacques

Conference (2011, September 23)

Classification trees built with the Classification And Regression Tree algorithm were evaluated for modelling infrared spectroscopic data in order to discriminate between genuine and counterfeit drug ... [more ▼]

Classification trees built with the Classification And Regression Tree algorithm were evaluated for modelling infrared spectroscopic data in order to discriminate between genuine and counterfeit drug samples and to classify counterfeit samples in different classes following the RIVM classification system. Models were built for two data sets consisting of the Fourier Transform Infrared spectra, the Near Infrared spectra and the Raman spectra for genuine and counterfeit samples of respectively Viagra® and Cialis®. Easy interpretable models were obtained for both models. The models were validated for their descriptive and predictive properties. The predictive properties were evaluated using both cross validation as an external validation set. The obtained models for both data sets showed a 100% correct classification for the discrimination between genuine and counterfeit samples and 83.3% and 100% correct classification for the counterfeit samples for the Viagra® and the Cialis® data set respectively. [less ▲]

Detailed reference viewed: 20 (1 ULg)
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See detailClassification trees based on infrared spectroscopic data to discriminate between genuine and counterfeit medicines
Deconinck, Eric; Sacré, Pierre-Yves ULg; Coomans, Danny et al

in Journal of Pharmaceutical & Biomedical Analysis (2012), 57(1), 68-75

Classification trees built with the Classification And Regression Tree algorithm were evaluated for modelling infrared spectroscopic data in order to discriminate between genuine and counterfeit drug ... [more ▼]

Classification trees built with the Classification And Regression Tree algorithm were evaluated for modelling infrared spectroscopic data in order to discriminate between genuine and counterfeit drug samples and to classify counterfeit samples in different classes following the RIVM classification system. Models were built for two data sets consisting of the Fourrier Transformed Infrared spectra, the Near Infrared spectra and the Raman spectra for genuine and counterfeit samples of respectively Viagra® and Cialis®. Easy interpretable models were obtained for both models. The models were validated for their descriptive and predictive properties. The predictive properties were evaluated using both cross validation as an external validation set. The obtained models for both data sets showed a 100% correct classification for the discrimination between genuine and counterfeit samples and 83.3% and 100% correct classification for the counterfeit samples for the Viagra® and the Cialis® data set respectively. [less ▲]

Detailed reference viewed: 59 (6 ULg)
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See detailClassification Trees based on infrared spectroscopic data to discriminate between genuine and counterfeit medicines.
Deconinck, Eric; Sacre, Pierre-Yves ULg; Coomans, Danny et al

Poster (2012)

Due to the extension of the internet, counterfeit drugs represent a growing threat for public health in the developing countries but also more and more in the industrial world. In literature several ... [more ▼]

Due to the extension of the internet, counterfeit drugs represent a growing threat for public health in the developing countries but also more and more in the industrial world. In literature several analytical techniques were applied in order to discriminate between genuine and counterfeit medecines. One thing all these techniques have in common is that they generate a huge amount of data, which is often difficult to interpret in order to see differences between the different samples and to determine the cause of the differences. The majority of the authors make use of explorative chemometric tools to visualise the differences in the data obtained for the different samples. Even if some of the applied methods could be able to give a model with predictive ability, only a few authors created a model able to predict if a sample is counterfeit or not. Classification trees built with the Classification And Regression Tree algorithm were evaluated for modelling infrared spectroscopic data in order to discriminate between genuine and counterfeit drug samples and to classify counterfeit samples in different classes following the RIVM classification system. Models were built for two data sets consisting of the Fourrier Transformed Infrared spectra, the Near Infrared spectra and the Raman spectra for genuine and counterfeit samples of respectively Viagra® and Cialis®. Easy interpretable models were obtained for both models. The models were validated for their descriptive and predictive properties. The predictive properties were evaluated using both cross validation as an external validation set. The obtained models for both data sets showed a 100% correct classification for the discrimination between genuine and counterfeit samples and 83.3% and 100% correct classification for the counterfeit samples for the Viagra® and the Cialis® data set respectively. [less ▲]

Detailed reference viewed: 22 (1 ULg)
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See detailClassification, photo-z and environment of X-ray selected sources in the XMM-LSS field
Melnyk, Olga ULg; Plionis, M; Elyiv, Andrii ULg et al

in Proceedings of the XXL Consortium meeting in the Castle of the Meudon observatory, from 9 to 13 July, 2012 (2012, July 09)

Detailed reference viewed: 31 (1 ULg)
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See detailClassifying microarray data with association rules
Antonie, Luiza; Bessonov, Kyrylo ULg

in Proceedings of the 2011 ACM Symposium on Applied Computing (2011)

Detailed reference viewed: 5 (1 ULg)
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See detailClassifying pairs with trees for biological network inference
Schrynemackers, Marie ULg

Conference (2012, November 28)

Detailed reference viewed: 10 (4 ULg)
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See detailClassifying pairs with trees for supervised biological network inference
Schrynemackers, Marie ULg; Wehenkel, Louis ULg; Madan Babu, Mohan et al

E-print/Working paper (2014)

Detailed reference viewed: 20 (3 ULg)
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See detailLe "classroom Assessment" dans l'enseignment supérieur : Diagnostiquer des faiblesses dans son enseignement
Poumay, Marianne ULg

Learning material (2005)

A l’origine de la professionnalisation de l’enseignement et de l’apprentissage, le courant de l’évaluation de classe (en anglais « classroom assessment ») est une composante d’un autre courant, celui de ... [more ▼]

A l’origine de la professionnalisation de l’enseignement et de l’apprentissage, le courant de l’évaluation de classe (en anglais « classroom assessment ») est une composante d’un autre courant, celui de la recherche en classe, né aux Etats-Unis au début des années 1990. Il a pour but d’aider les enseignants du supérieur à mieux comprendre ce que les étudiants apprennent à leur cours et avec quelle qualité ils apprennent. La recherche en classe pousse les enseignants à tenter de mieux comprendre les processus d’apprentissage en oeuvre chez leurs étudiants et à isoler les facteurs sur lesquels leur enseignement peut avoir une influence. [less ▲]

Detailed reference viewed: 23 (2 ULg)
See detailClaude Chabrol, le cinéaste aux mains sales
Tomasovic, Dick ULg

Conference given outside the academic context (2011)

Detailed reference viewed: 3 (1 ULg)
See detailClaude Lévi-Strauss et la philosophie
Delruelle, Edouard ULg

Book published by De Boeck-Université (1989)

Detailed reference viewed: 3 (0 ULg)
See detailClaude Lévi-Strauss. Notice nécrologique pour L'Année sartrienne
Cormann, Grégory ULg

in Cormann, Grégory (Ed.) L'Année sartrienne, n° 24 (2010)

Detailed reference viewed: 17 (7 ULg)
See detailClaude Strebelle, créateur d'un campus dans la nature
Frankignoulle, Pierre ULg

E-print/Working paper (2010)

Detailed reference viewed: 17 (8 ULg)
See detailClauis, Grammaire latine pour la lecture des auteurs, nouvelle édition revue et corrigée
Boxus, Anne-Marie; Lavency, Marius; Longrée, Dominique ULg et al

Book published by De Boeck (2014)

Detailed reference viewed: 32 (4 ULg)
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See detailLa clause (dite) de la "mortuaire" a encore de beaux jours devant elle
Dehalleux, Virginie ULg

in Actualités du Droit de la Famille (2011), 6

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See detailLa clause dite de la "mortuaire" plus vivante que jamais
Leleu, Yves-Henri ULg; Geelhand de Merxem, Nicolas

in Journal des Tribunaux (2011), (6423), 93-95

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See detailLa clause dite de la "mortuaire" validée et exemptée
Leleu, Yves-Henri ULg; Geelhand de Merxem, Nicolas

in Recueil Général de l'Enregistrement et du Notariat [= RGEN] (2011), 3

Detailed reference viewed: 34 (6 ULg)