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See detailMacaronesia: a source of hidden genetic diversity for post-glacial recolonization of western Europe in the leafy liverwort Radula lindenbergiana.
Laenen, Benjamin ULg; Desamore, Aurélie ULg; Devos, Nicolas et al

Poster (2010)

Aim Bryophytes exhibit the lowest rates of endemism among biota in Macaronesia and differ in diversity patterns from angiosperms by the widespread occurrence of endemics within and among archipelagos. In ... [more ▼]

Aim Bryophytes exhibit the lowest rates of endemism among biota in Macaronesia and differ in diversity patterns from angiosperms by the widespread occurrence of endemics within and among archipelagos. In this study, we test the hypothesis that high dispersal ability erodes phylogeographic signal and hampers the chances of diversification in bryophytes using the leafy liverwort Radula lindenbergiana as a model. Location Macaronesia, Europe, South Africa Methods 84 samples were collected across the species distribution range and sequenced at four cpDNA loci (atpB-rbcL, trnG, trnL, and rps4). Phylogenetic reconstructions and Bayesian ancestral area reconstructions were used in combination with population genetic statistics (H, Nst, Fst) to describe the pattern of present genetic diversity in R. lindenbergiana and infer its biogeographic history. Results The two regions with the highest haplotypic diversity are Madeira and the Canary Islands. Ancestral area reconstructions suggest that Macaronesia was colonized at least twice independently and that the haplotypes currently found in Western Europe share a Macaronesian common ancestor. Whilst analysis of molecular variance and Nst statistics indicate that present-day patterns of genetic variation have a globally significant biogeographic component, Fst values among Macaronesian archipelagos, North Africa, and the Iberian Peninsula, were not significant. Main conclusions The apparent lack of speciation amongst Macaronesian bryophytes hides actual patterns of diversification at the molecular level. The occurrence of Canarian endemic haplotypes across several islands, along with the non-significant Fst and Nst among islands, North Africa and the Iberian Peninsula, suggest intense dispersal. The occurrence of endemic haplotypes suggests, however, that dispersal does not completely prevent diversification. The high diversity found among Macaronesian haplotypes, together with the Macaronesian origin of all the haplotypes found in Western Europe, suggests that Macaronesian archipelagos could have served as a refugium during the Quaternary glaciations and as a source for re-colonization of Europe. [less ▲]

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See detailMachiavel
Herla, Anne ULg

Conference given outside the academic context (2010)

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See detailMachiavel et le paradigme foucaldien de la gouvernementalité
Herla, Anne ULg

Scientific conference (2006, March 03)

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See detailMachiavellian Rethoric. From the Counter-Reformation to Milton, Princeton.
Moreno, Paola ULg

in Revue Belge de Philologie et d'Histoire (1999)

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See detailLa machine à vapeur moderne (fin)
Dwelshauvers-Dery, Victor ULg

Speech (1903)

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See detailLa machine à vapeur moderne (suite)
Dwelshauvers-Dery, Victor ULg

Speech (1902)

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See detailLa machine à vapeur moderne
Dwelshauvers-Dery, Victor ULg

Speech (1901)

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See detailMachine Intelligence Technology for Automatic Target Recognition
Verly, Jacques ULg; Delanoy, Richard L.; Dudgeon, Dan E.

in Lincoln Laboratory Journal (1989), 2(2), 277-311

Detailed reference viewed: 29 (1 ULg)
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See detailMachine Intelligent Automatic Recognition of Critical Mobile Targets in Laser Radar Imagery
Delanoy, Richard L.; Verly, Jacques ULg; Dudgeon, Dan E.

in Lincoln Laboratory Journal (1993), 6(1), 161-186

Detailed reference viewed: 14 (4 ULg)
See detailMachine learning applied to power systems transient security functions
Wehenkel, Louis ULg; Xue, Yusheng; Van Cutsem, Thierry ULg et al

in Proc. IMACS Int. Symp. on AI, Experts Systems and Languages in Modelling and Simulation (1987)

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See detailA Machine Learning Approach for Material Detection in Hyperspectral Images
Marée, Raphaël ULg; Stevens, Benjamin ULg; Geurts, Pierre ULg et al

in Proc. 6th IEEE Workshop on Object Tracking and Classification Beyond and in the Visible Spectrum (OTCBVS-CVPR09) (2009)

In this paper we propose a machine learning approach for the detection of gaseous traces in thermal infra red hyperspectral images. It exploits both spectral and spatial information by extracting subcubes ... [more ▼]

In this paper we propose a machine learning approach for the detection of gaseous traces in thermal infra red hyperspectral images. It exploits both spectral and spatial information by extracting subcubes and by using extremely randomized trees with multiple outputs as a classifier. Promising results are shown on a dataset of more than 60 hypercubes. [less ▲]

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See detailA Machine Learning Approach to Improve Congestion Control over Wireless Computer Networks
Geurts, Pierre ULg; El Khayat, Ibtissam; Leduc, Guy ULg

(2004, November)

In this paper, we present the application of machine learning techniques to the improvement of the congestion control of TCP in wired/wireless networks. TCP is suboptimal in hybrid wired/wireless networks ... [more ▼]

In this paper, we present the application of machine learning techniques to the improvement of the congestion control of TCP in wired/wireless networks. TCP is suboptimal in hybrid wired/wireless networks because it reacts in the same way to losses due to congestion and losses due to link errors. We thus propose to use machine learning techniques to build automatically a loss classifier from a database obtained by simulations of random network topologies. Several machine learning algorithms are compared for this task and the best method for this application turns out to be decision tree boosting. It outperforms ad hoc classifiers proposed in the networking literature. [less ▲]

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See detailMachine Learning Approaches to Power System Security Assessment
Wehenkel, Louis ULg

Thèse d’agrégation de l’enseignement supérieur (1994)

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See detailMachine Learning Solution Methods for Multistage Stochastic Programming
Defourny, Boris ULg

Doctoral thesis (2010)

This thesis investigates the following question: Can supervised learning techniques be successfully used for finding better solutions to multistage stochastic programs? A similar question had already been ... [more ▼]

This thesis investigates the following question: Can supervised learning techniques be successfully used for finding better solutions to multistage stochastic programs? A similar question had already been posed in the context of reinforcement learning, and had led to algorithmic and conceptual advances in the field of approximate value function methods over the years. This thesis identifies several ways to exploit the combination "multistage stochastic programming/supervised learning" for sequential decision making under uncertainty. Multistage stochastic programming is essentially the extension of stochastic programming to several recourse stages. After an introduction to multistage stochastic programming and a summary of existing approximation approaches based on scenario trees, this thesis mainly focusses on the use of supervised learning for building decision policies from scenario-tree approximations. Two ways of exploiting learned policies in the context of the practical issues posed by the multistage stochastic programming framework are explored: the fast evaluation of performance guarantees for a given approximation, and the selection of good scenario trees. The computational efficiency of the approach allows novel investigations relative to the construction of scenario trees, from which novel insights, solution approaches and algorithms are derived. For instance, we generate and select scenario trees with random branching structures for problems over large planning horizons. Our experiments on the empirical performances of learned policies, compared to golden-standard policies, suggest that the combination of stochastic programming and machine learning techniques could also constitute a method per se for sequential decision making under uncertainty, inasmuch as learned policies are simple to use, and come with performance guarantees that can actually be quite good. Finally, limitations of approaches that build an explicit model to represent an optimal solution mapping are studied in a simple parametric programming setting, and various insights regarding this issue are obtained. [less ▲]

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See detailMachine learning techniques for atmospheric pollutant monitoring
Sainlez, Matthieu ULg; Heyen, Georges ULg

Poster (2012, January 27)

Machine learning techniques are compared to predict nitrogen oxide (NOx) pollutant emission from the recovery boiler of a Kraft pulp mill. Starting from a large database of raw process data related to a ... [more ▼]

Machine learning techniques are compared to predict nitrogen oxide (NOx) pollutant emission from the recovery boiler of a Kraft pulp mill. Starting from a large database of raw process data related to a Kraft recovery boiler, we consider a regression problem in which we are trying to predict the value of a continuous variable. Generalization is done on the worst case configuration possible to make sure the model is adequate: the training period concerns stationary operations while test periods mainly focus on NOx emissions during transient operations. [less ▲]

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See detailMachine learning techniques to assess the performance of a gait analysis system
Pierard, Sébastien ULg; Phan-Ba, Rémy; Van Droogenbroeck, Marc ULg

in European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) (2014, April 24)

This paper presents a methodology based on machine learning techniques to assess the performance of a system measuring the trajectories of the lower limbs extremities for the follow-up of patients with ... [more ▼]

This paper presents a methodology based on machine learning techniques to assess the performance of a system measuring the trajectories of the lower limbs extremities for the follow-up of patients with multiple sclerosis. We show how we have established, with the help of machine learning, four important properties about this system: (1) an automated analysis of gait characteristics provides an improved analysis with respect to that of a human expert, (2) after learning, the gait characteristics provided by this system are valuable compared to measures taken by stopwatches, as used in the standardized tests, (3) the motion of the lower limbs extremities contains a lot of useful information about the gait, even if it is only a small part of the body motion, (4) a measurement system combined with a machine learning tool is sensitive to intra-subject modifications of the walking pattern. [less ▲]

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See detailMachine learning, neural networks and statistical pattern recognition for voltage security: a comparative study
Wehenkel, Louis ULg; Van Cutsem, Thierry ULg; Pavella, Mania ULg et al

in International Journal of Intelligent Systems (1994), 2

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See detailMachine learning, neural networks and statistical pattern recognition for voltage security: a comparative study
Wehenkel, Louis ULg; Van Cutsem, Thierry ULg; Pavella, Mania ULg et al

in Proc. 5th International Conference on Intelligent System Applications to Power systems (1994, September)

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See detailMachine learning-based feature ranking: Statistical interpretation and gene network inference
Huynh-Thu, Vân Anh ULg

Doctoral thesis (2012)

Machine learning techniques, and in particular supervised learning methods, are nowadays widely used in bioinformatics. Two prominent applications that we target specifically in this thesis are biomarker ... [more ▼]

Machine learning techniques, and in particular supervised learning methods, are nowadays widely used in bioinformatics. Two prominent applications that we target specifically in this thesis are biomarker discovery and regulatory network inference. These two problems are commonly addressed through the use of feature ranking methods that order the input features of a supervised learning problem from the most to the less relevant for predicting the output. This thesis presents, on the one hand, methodological contributions around machine learning-based feature ranking techniques and on the other hand, more applicative contributions on gene regulatory network inference. Our methodological contributions focus on the problem of selecting truly relevant features from machine learning-based feature rankings. Unlike the p-values returned by univariate tests, relevance scores derived from machine learning techniques to rank the features are usually not statistically interpretable. This lack of interpretability makes the identification of the truly relevant features among the top-ranked ones a very difficult task and hence prevents the wide adoption of these methods by practitioners. Our first contribution in this field concerns a procedure, based on permutation tests, that estimates for each subset of top-ranked features the probability for that subset to contain at least one irrelevant feature (called CER for "conditional error rate"). As a second contribution, we performed a large-scale evaluation of several, existing or novel, procedures, including our CER method, that all replace the original relevance scores with measures that can be interpreted in a statistical way. These procedures, which were assessed on several artificial and real datasets, differ greatly in terms of computing times and the tradeoff they achieve in terms of false positives and false negatives. Our experiments also clearly highlight that using model performance as a criterion for feature selection is often counter-productive. The problem of gene regulatory network inference can be formulated as several feature selection problems, each one aiming at discovering the regulators of one target gene. Within this family of methods, we developed the GENIE3 algorithm that exploits feature rankings derived from tree-based ensemble methods to infer gene networks from steady-state gene expression data. In a second step, we derived two extensions of GENIE3 that aim to infer regulatory networks from other types of data. The first extension exploits expression data provided by time course experiments, while the second extension is related to genetical genomics datasets, which contain expression data together with information about genetic markers. GENIE3 was best performer in the DREAM4 In Silico Multifactorial challenge in 2009 and in the DREAM5 Network Inference challenge in 2010, and its extensions perform very well compared to other methods on several artificial datasets. [less ▲]

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See detailMachine perfusion in clinical trials : "machine vs. solution effects"
Treckmann, Jürgen; Moers, Cyril; Smits, Jacqueline M et al

in Transplant International (2012), 25

Detailed reference viewed: 15 (0 ULg)