Éditeur(s) : HAL CCSD
Résumé : International audience
In this paper, we describe a blood alcohol content estimation prototype based on a comportment analysis performed by artificial neural networks. We asked to subjects that had drunk alcohol to play a video-game after having measured their blood alcohol content with a breathalyser. A racing game was modified so that it could provide various data related to the use of the controls by the player. Using the collected data, we trained our neural network in order to be able to determine whether or not the subject had exceeded a blood alcohol content threshold. We also succeeded in estimating this blood alcohol content with a mean error of 0.1g/l. We could perform those estimations independently of the track played among the two ones used. It was also performed in "real time", e.g., using only the data collected within the last 10 seconds of playing.
Proceedings of the 2011 International Conference on Advances in Computer-Human Interactions
Gosier, Guadeloupe

Droits : info:eu-repo/semantics/OpenAccess
hal-00849971
https://hal.archives-ouvertes.fr/hal-00849971
https://hal.archives-ouvertes.fr/hal-00849971/document
https://hal.archives-ouvertes.fr/hal-00849971/file/article.pdf

Éditeur(s) : HAL CCSD
Résumé : International audience
In this work, we have led an analysis of the error of different global solar radiation prediction models according to the global solar radiation variability. Different predictions models where performed such as machine learning techniques (Neural Networks, Gaussian processes and support vector machines) in order to forecast the Global Horizontal solar Irradiance (GHI). We also include in this study a simple linear autoregressive (AR) model as well as two naive models based on persistence of the GHI and persistence of the clear sky index (denoted herein scaled persistence model). The models are calibrated and tested with data from three 3 French islands: Corsica (42.15°N ; 9.08°E), Guadeloupe (16.25°N ; 61.58°W) and Reunion (21.15°S ; 55.5°E). Guadeloupe and Reunion are located in a subtropical climatic zone whereas Corsica is in a tempered climatic zone. Hence the global solar radiation variation differs significantly. The output error of the different models was quantified by the nRSME. In order to quantify the influence of the global solar radiation variability on the forecasting models error we performed a classification of typical days according to different typical days. Each class of typical day is defined by a variation of global solar radiation rate. For each class and for each location, the selected forecasting models where performed and the error was quantified. With this analysis a global solar radiation forecasting models can be selected according to the location, the global solar radiation fluctuations and hence the meteorological conditions. INTRODUCTION Large and frequent variations of solar radiation can be observed in tropical climates with amplitudes reaching 800 W/m² and occurring within a short time interval, from few seconds to few minutes, according to the geographical location. Such fluctuations can be due for example to the dynamic of clouds which can be very complex and depend on cloud type, size, speed and spatial distribution and, more generally, due to some specific local meteorological conditions. Thus, the solar energy forecasting, a process used to predict the amount of solar energy available in the current and near terms, might be a difficult task. Some of the best predictors found in literature are Autoregressive and moving average (ARMA) [5,7,8], Bayesian inferences [9,10], Markov chains [11], k-Nearest-Neighbors predictors [12] or artificial intelligence techniques as the Artificial Neural Network (ANN) [9-11]. Although these methodologies are potentially good in many areas, we observed in our previous studies on global radiation prediction [9,13,14] that the simple model based on the persistence of the clear sky index gives often very good results with acceptable errors [15] for short term forecasting time horizon (<= 1 hour). The goal of this paper is to determinate the influence of solar radiation variability regarding different classes of days on the expected error provided by different forecasting methods that the modeller can possibly implement. The paper is organized as follow: Section 2 describes the data we have used. Section 3 exposes the classification methodology and the results obtained for the three studied locations. In the two following sections, the forecasting methods are exposed and then 3 the errors on the forecasting results for each location and for each class are exposed.
The Third Southern African Solar Energy Conference (SASEC2015)
Kruger National Park, South Africa

Droits : info:eu-repo/semantics/OpenAccess
hal-01099487
https://hal.archives-ouvertes.fr/hal-01099487
https://hal.archives-ouvertes.fr/hal-01099487/document
https://hal.archives-ouvertes.fr/hal-01099487/file/article%20SASEC.pdf

Éditeur(s) : HAL CCSD
Résumé : International audience
It is now well recognized that information management systems often deal with geographic information even if they often do not take advantage of it. In these systems, requirements engineering (RE) is becoming more and more important in helping stakeholders to model their requirements regarding the quality of the data.However, to date, few RE projects take geographic information and geographic constraints into account. We thus propose to study in this paper the question of managing such geographic constraints in the framework of RE. We propose an extension of the RE model and its implementation within the KAOS framework, and the use of geographic ontologies
2nd International Conference on Knowledge Management, Information and Knowledge Systems
Hammamet, Tunisia

lirmm-01230140
https://hal-lirmm.ccsd.cnrs.fr/lirmm-01230140

Éditeur(s) : HAL CCSD
Résumé : International audience
We instrumented a car simulator by gathering low level data and fed it to an artificial neural network in order to perform blood alcohol content (BAC) estimations. The results depend on the quality of the data extraction and processing, and also on the selected inputs. We explain our data extraction and processing methodology, and how we used it to generate reliable and comparable features. At last, we describe the performances of individual features and how they combine. In the end, the prototype was able to accurately estimate the BAC value of a subject after being trained with driving samples of this subject with various BAC values.
Proceedings of ACHI 2013, The Sixth International Conference on Advances in Computer-Human Interactions
Nice, France

hal-00850137
https://hal.archives-ouvertes.fr/hal-00850137

Éditeur(s) : HAL CCSD IRD
Résumé : International audience
The number of satellites and remote sensing sensors devoted to earth observation becomes increasingly high, providing more and more data and especially images. In the same time the access to such a data and to the tools to process them has been considerably improved. In the presence of such data flow - and regarding the necessity to follow up and predict environmental and societal changes in highly dynamic socio-environmental contexts - we need automatic image interpretation methods. This could be accomplished by exploring some strengths of artificial intelligence. Our main idea consists in inducing classification rules that explicitly take into account structural knowledge, using Aleph, an Inductive Logic Programming (ILP) system. We applied our proposed methodology to three land cover/use maps of the French Guiana littoral. One hundred and forty six classification rules were induced for the 39 land-cover classes of the maps. These rules are expressed in first order logic language which make them intelligible and interpretable by non-experts. A ten-fold cross validation gave average values for classification accuracy, specificity and sensibility equal to, respectively, 98.82 %, 99.65% and 70%. The proposed methodology could be valuably exploited to automatically classify new objects and/or help operators using object-based classification procedures.
Symposium of the Latin American Society for Remote Sensing and Spatial Information Systems (SELPER)
Cayenne, French Guiana

ird-00797337
http://hal.ird.fr/ird-00797337
http://hal.ird.fr/ird-00797337/document
http://hal.ird.fr/ird-00797337/file/Bayoudh_et_al_2012_FP_SELPER-086.pdf
IRD : fdi:010058343

Éditeur(s) : HAL CCSD
Résumé : International audience
“Affective Computing is computing that relates to, arises from, or deliberately influences emotions” (R.W. Picard, MIT Press, 1997). Specifically, psychosocial equilibrium is now considered as a major issue for both individual and public health. Predicting human emotions via non-intrusive methods is a great challenge triggered by the rise of intelligent assisting systems. In order to determine the emotional state of a subject having a potential psychosocial disorder, it is required to process both neutral and expressive emotions. A computer-engineered method aiming to provide emotional information from videos is proposed. An original approach for affect classification and its interpretation is presented.The database is part of the HUMAINE database, and consists of a large volume of video clips, each displaying individuals, depressed or not, talking with a conversational agent. Both the high-dimensionality of data and its temporal aspect require a pre-training that consists of cleaning, sorting and feature selection, in order to make relevant information usable for further processing by MachineLearning methods. Next step consists in classifying the affective state of subjects according to their positivity or negativity (Russell's model, 1980). By means of six independent classifiers, each running a Support Vector Machine algorithm, separate performances are obtained for each class of emotions. The results reveal an interesting “neutral area phenomenon”, where emotions tend to be harder to be detected.Though more frequent, neutral emotions seem to carry less discriminant information than expressive ones. This issue will be a key for designing a tuning step in further research.
Caribbean Academy Of Science
Deshaies, France

hal-01408029
https://hal.univ-antilles.fr/hal-01408029

Résumé : Les travaux de recherche présentés dans ce mémoire s'inscrivent dans la lignée des approches de modélisation hydrologiques prédictives dirigées par les données. Nous avons particulièrement développé leur application sur le contexte difficile des phénomènes de crue éclairs caractéristiques des bassins versants de la région Caraïbe qui pose un dé fi sé.curi taire. En envisageant le problème de la prévision de crues comme un problème d'optimisation combinatoire difficile nous proposons d'utiliser la notion de métaneuristiques, à travers les algorithmes évolutionnaire notamment pour leur capacité à parcourir efficacement de grands espaces de recherche et fi fournir des solutions de bOlIDe qualité en des temps d'exécution raisonnables. Nous avons présenté l'approche de prédiction AV2D : Aggregate Variable Data Driven dom le concept central est la notion de variable agrégée. L'idée sous-jacente à ce concept est de considérer le pouvoir prédictif de nouvelles variables définies comme le résultat de fonctions tatistiques, dites d'agrégation calculées sur de donnée' correspondant à des périodes de temps précédent uo événem nt à prédire. Ces variable sont caractérisées par des ensembles de paramètres correspondant a leur pJ:opriétés. Nous avons imroduitle variables agrégées hydrométéorologiques permettant de répondre au problème de la classification d événements hydrologiques. La complexité du parcours de l'espace de recherche engendré par les paramètres définissant ces variables a été prise en compte grâce à la njse en oeuvre d'un algorithme évolutionnaire particulier dont les composants ont été spécifiquement définis pour ce problème. Nous avons montré, à travers une étude comparative avec d'autres approches de modélisation dirigées par les données, menée sur deux cas d'études de bassins versant caribéens, que l'approche AV2D est particulièrement bien adaptée à leur contexte. Nous étudions par la suite les bénéfices offerts par les approches de modélisation hydrologiques modulaires dirigées par les données, en définissant un procédé de division en sous-processus prenant en compte les caractéristiques paniculières des bassins versants auxquels nous nous intéressons. Nou avons proposé une extension des travaux précédents à travers la définition d'une approche de modélisation modulaire M2D: Spatial Modular Data Driven, consistant à considérer des sous-processus en divisant l'ensemble des exemples à classifier en sous-ensembles correspondant à des comportements hydrologiques homogènes. Nous avons montré à travers une étude comparative avec d autres approches dU'igées par les données mises en oeuvre sur les mêmes sous-ensembles de données que celte approche permet d améliorer les résultats de prédiction particulièrement à coun Lenne. Nous avons enfin proposé la modélisation d un outil de pi
The work presented in this thesis is in the area of data-driven hydrological modeling approaches. We particularly investigared their application on the difficult problem of flash flood phenomena typically observed in Caribbean watersheds. By considering the problem of flood prediction as a combinatorial optimization problem, we propose to use the notion of Oleraheuristics, through evolutionary algorithms, especially for their capacity ta visit effjciently large search space and to provide good solutions in reasonable execution times. We proposed the hydrological prediction approach AV2D: Aggregate Variable Data Driven which central concept is the notion of aggregate variable. The underlying idea of this [concept is to consider the predictive power of new variables defined as the results of statistical functions, called aggregation functions, computed on data corresponding ta time periods before an event ta predict. These variables are characterized by sets of parameters corresponding ta their specifications. We introduced hydro-meteorological aggregate variables allowing ta address the classification problem of hydrological events. We showed through a comparative study on two typical caribbean watersheds, using several common data driven modelling techniques that the AV2D approach is panicul.rly weil fitted ta the studied context. We also study the benefits offered by modulaI' approaches through the definition of the SM2D: Spatial Modular DataDriven approach, consisting in considering sub-processes partly defined by spatial criteria. We showed that the results obtained by the AV2D on these sub-processes allows to increase the performances particularly for short term prediction. Finally we proposed the modelization of a generic control tool for hydro-meteorological prediction systems, H2FCT: Hydro-meteorological Flood Forecasting Control 1'001

http://www.theses.fr/2013AGUY0673/document

Éditeur(s) : HAL CCSD
Résumé : International audience
One of the most common tasks in the area of social network mining is the extraction of frequent patterns from social networks. Although traditional approaches have been mainly focused on subgraphs occurring frequently in a network or a set of networks, new approaches have attempted to exploit network structure and node properties in order to elicit new kinds of patterns. One of these news approaches is the extraction of conceptual links, a solution that combines both structure and node properties for providing knowledge on the groups of nodes the most connected in a social network. However, if the extraction of conceptual links offers a great potential in terms of knowledge discovery as well as network visualization, the search for these kinds of patterns remains a computationally intensive problem. Thus, the efficiency of the extraction processes highly depends on the efficiency of the underlying algorithm. In this paper, we focus on the extraction of maximal frequent conceptual links (MFCL) and we propose a hybrid algorithm that applies a filter on the nodes for reducing the search space to the most frequent groups of nodes. Although our solution potentially causes loss of some patterns, we demonstrate its efficiency by comparison with the MFCLMin algorithm. We investigate the potential loss of patterns, the gain on the runtime, and the gain on the number of comparisons.
IEEE International Conference on Research Challenges in Information Science
Paris, France

hal-00841699
https://hal.archives-ouvertes.fr/hal-00841699

Éditeur(s) : HAL CCSD IEEE
Résumé : International audience
In this paper, we present an evolutionary approach for extracting a model of flood prediction from hydrological data observed timely on water heights in a river watershed. Since this kind of data recorded by sensors on river basins is highly scarce and hopefully much unbalanced between cases of floods and non-floods, we have adopted the notion of aggregate variables which values are computed as aggregates on raw data. An evolutionary algorithm is involved to allow selecting the best sets - juries of classifiers- of such variables as predictive variables. Two real hydrological data sets are trained and they both show the efficiency of the method compared to traditional solutions for prediction.
IEEE Congress on Evolutionary Computation 2012
Brisbane, Australia

hal-00840739
https://hal.archives-ouvertes.fr/hal-00840739
DOI : 10.1109/CEC.2012.6252897

Éditeur(s) : HAL CCSD IEEE
Résumé : International audience
This paper proposes a novel automated approach for the categorization of cells in fluorescence microscopy images. Our supervised classification method aims at recognizing patterns of unlabeled cells based on an annotated dataset. First, the cell images need to be indexed by encoding them in a feature space. For this purpose, we propose tailored bio-inspired features relying on the distribution of contrast information. Then, a supervised learning algorithm is proposed for classifying the cells. We carried out experiments on cellular images related to the diagnosis of autoimmune diseases, testing our classification method on the HEp-2 Cells dataset of Foggia et al (CBMS 2010). Results show classification precision larger than 96% on average, thus confirming promising application of our approach to the challenging application of cellular image classification for computer-aided diagnosis.
ICPR 2012
Tsukuba, Japan

hal-00958856
https://hal.archives-ouvertes.fr/hal-00958856
https://hal.archives-ouvertes.fr/hal-00958856/document
https://hal.archives-ouvertes.fr/hal-00958856/file/icpr_2012.pdf

Éditeur(s) : HAL CCSD
Résumé : International audience
This paper describes a blood alcohol content estimation method for car driver, based on a comportment analysis performed within a realistic simulation. An artificial neural network learns how to estimate subject's blood alcohol content. Low-level recording of user actions on the steering wheel and pedals are used to feed a multilayer perceptron, and a breathalyzer is used to build the learning examples set (desired output). Results are compared with a successful previous work based on a simple video game and demonstrate the ''complexity scalability'' of the approach.
The 20 th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Proceedings - Bruges, Belgium from 25 to 27 April 2012 .
Bruges, Belgium

hal-00850136
https://hal.archives-ouvertes.fr/hal-00850136

Éditeur(s) : HAL CCSD Elsevier
Résumé : International audience
The number of satellites and sensors devoted to Earth observation has become increasingly elevated, delivering extensive data, especially images. At the same time, the access to such data and the tools needed to process them has considerably improved. In the presence of such data flow, we need automatic image interpretation methods, especially when it comes to the monitoring and prediction of environmental and societal changes in highly dynamic socio-environmental contexts. This could be accomplished via artificial intelligence. The concept described here relies on the induction of classification rules that explicitly take into account structural knowledge, using Aleph, an Inductive Logic Programming (ILP) system, combined with a multi-class classification procedure. This methodology was used to monitor changes in land cover/use of the French Guiana coastline. One hundred and fifty-eight classification rules were induced from 3 diachronic land cover/use maps including 38 classes. These rules were expressed in first order logic language, which makes them easily understandable by non-experts. A 10-fold cross-validation gave significant average values of 84.62%, 99.57% and 77.22% for classification accuracy, specificity and sensitivity, respectively. Our methodology could be beneficial to automatically classify new objects and to facilitate object-based classification procedures.
ISSN: 0098-3004

hal-01369822
https://hal.archives-ouvertes.fr/hal-01369822
https://hal.archives-ouvertes.fr/hal-01369822/document
https://hal.archives-ouvertes.fr/hal-01369822/file/CAGEO-D-13-00538R1_article.pdf
DOI : 10.1016/j.cageo.2014.08.013

Éditeur(s) : HAL CCSD
Résumé : Session "Articles"
National audience
Dans cet article, nous présentons une méthode robuste et précise pour détecter 17 points caractéristiques du visage sur des images expressives. Une nouvelle architecture multi-résolution basée sur les récents algorithmes multi-noyau est introduite. Les patches de faibles résolutions codent les informations globales du visage donnant lieu à une détection grossière mais robuste du point désiré. Les patches de grandes résolutions quant à eux utilisent les détails locaux afin d'affiner cette localisation. En combinant une détection indépendante de points et des informations a priori sur les distributions de points, nous proposons un détecteur robuste aux changements d'expressions ainsi qu'aux variations d'illuminations. Ce système a été évalué sur plusieurs bases de données de la littérature. Les résultats présentés améliorent les performances des détecteurs de l'état de l'art
Actes de la conférence RFIA 2012
Lyon, France

hal-00656522
https://hal.archives-ouvertes.fr/hal-00656522
https://hal.archives-ouvertes.fr/hal-00656522/document
https://hal.archives-ouvertes.fr/hal-00656522/file/rfia2012_submission_99.pdf

Éditeur(s) : HAL CCSD
Résumé : Session "Articles"
National audience
Nous présentons ici une méthode permettant de créer et de représenter sous forme vectorielle des classes de transition à partir d'une classification supervisée basée sur des arbres de décision flous. Cette méthode est particulièrement utile pour la classification stricte de données dont les frontières sont naturellement diffuses ainsi que pour leur représentation au sein d'un System d'Information Géographique. C'est le cas par exemple des formations forestières entre lesquels les transitions sont généralement étalées sur plusieurs centaines de mètres avec des gradients dépendant de condition environnementales locales ne permettant pas de définir une transition identique dans toutes les directions. La méthode présentée permet d'ajouter des classes lorsque les zones de transition sont trop étendues plutôt que de fixer une frontière arbitrairement
Actes de la conférence RFIA 2012
Lyon, France

Droits : info:eu-repo/semantics/OpenAccess
hal-00656502
https://hal.archives-ouvertes.fr/hal-00656502
https://hal.archives-ouvertes.fr/hal-00656502/document
https://hal.archives-ouvertes.fr/hal-00656502/file/rfia2012_submission_57.pdf

Éditeur(s) : HAL CCSD springer
Résumé : International audience
In this paper, we address the problem of flood prediction in complex situations. We present an original solution in order to achieve the main goals of accuracy, flexibility and readability. We propose the SM2D modular data driven approach that provides predictive models for each sub-process of a global hydrological process. We show that this solution improves the predictive accuracy regarding a global approach. The originality of our proposition is threefold: (1) the predictive model is defined as a set of aggregate variables that act as classifiers, (2) an evolutionary technique is implemented to find best juries of such classifiers and (3) the flood process complexity problem is addressed by searching for sub-models on sub-processes identified partly by spatial criteria. The solution has proved to perform well on flash flood phenomena of tropical areas known to be hardly predictable. It was indeed successfully applied on a real caribbean river dataset after both preprocessing and preliminary analysis steps presented in the paper.
Lecture notes in computer science
Singapour, Singapore

hal-00878780
https://hal.archives-ouvertes.fr/hal-00878780
DOI : 10.1007/978-3-642-40897-7_13

Éditeur(s) : HAL CCSD
Résumé : Session "Atelier IHMA"
National audience
Nous présentons une méthode pour la simulation des capacités d'évaluation de la dissemblance de stratégies de construction de dessins. Cette méthode exploite un descripteur structurel des procédures, l'OPSRL, ainsi qu'une mesure de dissemblance que nous présentons. La polyvalence des types de traitements qui peuvent être mis en oeuvre grâce à cette méthode est illustrée sur des jeux de tracés de formes géométriques qui ont été acquis en ligne au moyen de tablettes à digitaliser.
Actes de la conférence RFIA 2012
Lyon, France

Droits : info:eu-repo/semantics/OpenAccess
hal-00660983
https://hal.archives-ouvertes.fr/hal-00660983
https://hal.archives-ouvertes.fr/hal-00660983/document
https://hal.archives-ouvertes.fr/hal-00660983/file/IHMA_06_-_Ney_-_Remi.pdf

Éditeur(s) : HAL CCSD
Résumé : J'étais à l'époque dans le laboratoire I3S de Sophia-Antipolis. Je suis dorénavant au laboratoire GRIMAAG en Guadeloupe.
We focused on the communication problem within multi-agents systems, inspirited by nature and more precisely by the ants that are a perfect example of living integrating learning (ontogenesis) to genetic inheritance (phylogenesis): the Baldwin Effect.
We worked with agents represented by classifiers systems using genetic algorithm. We introduced a new fixed knowledge based classifiers system, the simplified Pittsburgh classifier system, which we studied in a spontaneous coordination multi-agents problem. Then, we successfully allowed “homogeneous” agents to communicate their “knowledge” using distributed elitism.
Finally, “heterogeneous” agents efficiently exchanged information thanks to a minimal communication model we defined, bound to reality and extended.
Nous nous sommes intéressés à la problématique de la communication au sein des systèmes multi-agents en nous inspirant de la nature et plus particulièrement des fourmis qui sont un parfait exemple de l'intégration de l'apprentissage du vivant (ontogenèse) au capital génétique de celui-ci (phylogénie) : l'effet Baldwin.
Nous avons choisi de travailler avec des systèmes de classeurs à algorithme génétique pour représenter les agents. Nous avons introduit un nouveau type de système de classeurs à base de connaissance de taille fixe, les systèmes de classeurs de type Pittsburgh simplifié, dont nous avons étudié les propriétés dans un contexte multi-agents de coordination spontanée.
Nous avons ensuite proposé avec succès aux agents "homogènes" de communiquer pour échanger leur "connaissance" à l'aide de l'élitisme distribué.
Enfin, les agents "hétérogènes" ont échangé efficacement des informations grâce à un modèle minimal de communication que nous avons ancré dans la réalité puis étendu.
https://tel.archives-ouvertes.fr/tel-00192372

tel-00192372
https://tel.archives-ouvertes.fr/tel-00192372
https://tel.archives-ouvertes.fr/tel-00192372/document
https://tel.archives-ouvertes.fr/tel-00192372/file/These.pdf

Éditeur(s) : HAL CCSD
Résumé : Document soutenu à l'atelier Fouille de Données Complexes de la conférence EGC2017
International audience
"L'informatique affective est l'informatique qui se réfère à, résulte de, ou influence délibérément les émotions" (Picard (1997)). Singulièrement, l'équi-libre psychosocial est désormais considéré comme une préoccupation majeure pour la santé, tant sur le plan individuel que public. Prédire les émotions hu-maines via des méthodes non-invasives est un challenge propulsé notamment par le développement des systèmes d'accompagnement intelligents. Afin de dé-terminer l'état émotionel d'un individu, ayant potentiellement un trouble psy-chosocial, il convient de traiter tant les émotions expressives que neutres. Une approche originale de classification de l'information émotionelle à partir de vi-déos est proposée.
Extraction et Gestion des Connaissances
Grenoble, France

Droits : http://creativecommons.org/licenses/by-nc-sa/
hal-01513329
https://hal.archives-ouvertes.fr/hal-01513329
https://hal.archives-ouvertes.fr/hal-01513329/document
https://hal.archives-ouvertes.fr/hal-01513329/file/egcArticle%20scholet.pdf

Éditeur(s) : HAL CCSD D-side publications, Evere, Belgium
Résumé : International audience
This article underlines the learning and discrimination capabilities of a model of associative memory based on artificial networks of spiking neurons. Inspired from neuropsychology and neurobiology, the model implements top-down modulations, as in neocortical layer V pyramidal neurons, with a learning rule based on synaptic plasticity (STDP), for performing a multimodal association learning task. A temporal correlation method of analysis proves the ability of the model to associate specific activity patterns to different samples of stimulation. Even in the absence of initial learning and with continuously varying weights, the activity patterns become stable enough for discrimination.
Proceedings of 14 European Symposium on Artificial Neural Networks (ESANN 2006)

hal-00115420
https://hal.archives-ouvertes.fr/hal-00115420
https://hal.archives-ouvertes.fr/hal-00115420/document
https://hal.archives-ouvertes.fr/hal-00115420/file/esann06_HAL.pdf
ARXIV : cs.NE/0611104

Éditeur(s) : HAL CCSD ACM
Résumé : International audience
In this study, we use an adapted version of Pitsburgh-like learning classifier system to perform over classification tasks. The Adapted Pittsburgh Classifier System, enhanced with a new mechanism, allows us to consider the classification problems and their treatment by a given LCS in a different manner. Our aim is to exhibit elements in order to prove that, using the action covering mechanism, this system is able to build an inner map of a given classification learning sample. In the context of this paper, this map is built using an intrisic property of Pittsburgh-like CS: the use of various collections of classifiers amongst a unique population.
Proceedings of the 12th annual conference comp on Genetic and evolutionary computation
Portland,Oregon, United States

hal-00520609
https://hal.archives-ouvertes.fr/hal-00520609
DOI : 10.1145/1830761.1830823