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See detailLe "Machiavel" de Lefort
Mancuso, Eva ULg

Scientific conference (2014, November 07)

Detailed reference viewed: 12 (1 ULg)
Peer Reviewed
See detailMachiavellian Rethoric. From the Counter-Reformation to Milton, Princeton.
Moreno, Paola ULg

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

Detailed reference viewed: 24 (3 ULg)
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See detailLa machine à vapeur moderne (fin)
Dwelshauvers-Dery, Victor ULg

Speech/Talk (1903)

Detailed reference viewed: 31 (2 ULg)
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See detailLa machine à vapeur moderne (suite)
Dwelshauvers-Dery, Victor ULg

Speech/Talk (1902)

Detailed reference viewed: 27 (2 ULg)
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See detailLa machine à vapeur moderne
Dwelshauvers-Dery, Victor ULg

Speech/Talk (1901)

Detailed reference viewed: 129 (2 ULg)
Peer Reviewed
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: 32 (1 ULg)
Peer Reviewed
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: 17 (4 ULg)
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See detailMachine Learning and Insulin Sensitivity in Determining Outcome in Preterm Infants
Uyttendaele, Vincent; Dickson, JL; Lynn, A et al

Poster (2015)

Detailed reference viewed: 28 (1 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)

Detailed reference viewed: 11 (0 ULg)
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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 ▲]

Detailed reference viewed: 74 (18 ULg)
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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 ▲]

Detailed reference viewed: 49 (4 ULg)
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See detailMachine Learning Approaches to Power System Security Assessment
Wehenkel, Louis ULg

Post doctoral thesis (1994)

Detailed reference viewed: 29 (5 ULg)
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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 ▲]

Detailed reference viewed: 170 (18 ULg)
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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 ▲]

Detailed reference viewed: 29 (8 ULg)
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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 ▲]

Detailed reference viewed: 75 (14 ULg)
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See detailMachine Learning to Balance the Load in Parallel Branch-and-Bound
Marcos Alvarez, Alejandro ULg; Wehenkel, Louis ULg; Louveaux, Quentin ULg

E-print/Working paper (2015)

We describe in this paper a new approach to parallelize branch-and-bound on a certain number of processors. We propose to split the optimization of the original problem into the optimization of several ... [more ▼]

We describe in this paper a new approach to parallelize branch-and-bound on a certain number of processors. We propose to split the optimization of the original problem into the optimization of several subproblems that can be optimized separately with the goal that the amount of work that each processor carries out is balanced between the processors, while achieving interesting speedups. The main innovation of our approach consists in the use of machine learning to create a function able to estimate the difficulty (number of nodes) of a subproblem of the original problem. We also present a set of features that we developed in order to characterize the encountered subproblems. These features are used as input of the function learned with machine learning in order to estimate the difficulty of a subproblem. The estimates of the numbers of nodes are then used to decide how to partition the original optimization tree into a given number of subproblems, and to decide how to distribute them among the available processors. The experiments that we carry out show that our approach succeeds in balancing the amount of work between the processors, and that interesting speedups can be achieved with little effort. [less ▲]

Detailed reference viewed: 81 (9 ULg)
Peer Reviewed
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

Detailed reference viewed: 23 (2 ULg)
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)

Detailed reference viewed: 26 (1 ULg)
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See detailA Machine Learning-Based Approximation of Strong Branching
Marcos Alvarez, Alejandro ULg; Louveaux, Quentin ULg; Wehenkel, Louis ULg

in INFORMS Journal on Computing (in press)

We present in this paper a new generic approach to variable branching in branch-and-bound for mixed- integer linear problems. Our approach consists in imitating the decisions taken by a good branching ... [more ▼]

We present in this paper a new generic approach to variable branching in branch-and-bound for mixed- integer linear problems. Our approach consists in imitating the decisions taken by a good branching strategy, namely strong branching, with a fast approximation. This approximated function is created by a machine learning technique from a set of observed branching decisions taken by strong branching. The philosophy of the approach is similar to reliability branching. However, our approach can catch more complex aspects of observed previous branchings in order to take a branching decision. The experiments performed on randomly generated and MIPLIB problems show promising results. [less ▲]

Detailed reference viewed: 58 (4 ULg)