Bibliografie tematiche / Classification probabiliste

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Letteratura scientifica selezionata sul tema "Classification probabiliste"

Autore: Grafiati
Pubblicato: 12 ottobre 2024
Ultima modifica: 31 luglio 2025

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Articoli di riviste sul tema "Classification probabiliste"

1

Mselati, Benoit. "Classification et représentation probabiliste des solutions positives d'une équation elliptique semi-linéaire." Comptes Rendus Mathematique 335, no. 9 (2002): 733–38. http://dx.doi.org/10.1016/s1631-073x(02)02557-8.

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Garbolino, Emmanuel, Patrice De Ruffray, Henry Brisse, and Gilles Grandjouan. "Les phytoclimats de France : classification probabiliste de 1874 bio-indicateurs du climat." Comptes Rendus Biologies 331, no. 11 (2008): 881–95. http://dx.doi.org/10.1016/j.crvi.2008.08.009.

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Stan, Emanuela, Camelia-Oana Muresan, Raluca Dumache, et al. "Sex Estimation from Computed Tomography of Os Coxae—Validation of the Diagnose Sexuelle Probabiliste (DSP) Software in the Romanian Population." Applied Sciences 14, no. 10 (2024): 4136. http://dx.doi.org/10.3390/app14104136.

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This study aimed to evaluate the DSP method’s applicability to Romania’s contemporary population and to assess the accuracy and reliability of variables derived from CT images. A total of 80 pelvic CT scans were analyzed. Participants ranged from 22 to 93 years, with a mean age of 59.51 ± 22.7 years. All variables measured from the CT scans were analyzed using DSP software. The study found that sex estimation was possible in 71.25% of cases overall, with varying rates between males (57.50%) and females (85%). Despite encountering undetermined specimens comprising 42.5% males and 15% females, o
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Gamboa, Luis Fernando. "Strategic Uses of Mobile Phones in the BoP: Some Examples in Latin American Countries." Lecturas de Economía, no. 71 (February 23, 2010): 209–34. http://dx.doi.org/10.17533/udea.le.n71a4820.

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El objetivo del trabajo es analizar el uso de un conjunto de estrategias para minimizar el gasto en telefonía móvil en una encuesta de telefonía móvil para personas de bajos ingresos en Argentina, Brasil, Colombia, México y Perú. La metodología empleada incluye dos etapas; primero, se evalúa cuáles son los determinantes del uso de cada estrategia mediante un modelo probabilístico y se encuentra que la edad y el nivel de escolaridad influyen positivamente en la probabilidad de usar las alternativas; segundo, se utiliza un modelo de Poisson para evaluar el número de estrategias utilizadas. Aunqu
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Yerokhin, A. L., and O. V. Zolotukhin. "Fuzzy probabilistic neural network in document classification tasks." Information extraction and processing 2018, no. 46 (2018): 68–71. http://dx.doi.org/10.15407/vidbir2018.46.068.

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Ousmane, Cissé. "Diagnostic spatial et Perception des agriculteurs sur la fragmentation de la Forêt Classée De Mbao (FCM), Dakar, Sénégal." Revue Ecosystèmes et Paysages 4, no. 2 (2024): 1–10. https://doi.org/10.59384/recopays.tg4214.

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Aux défis de la fragmentation des écosystèmes naturels et son corolaire de perte de la biodiversité ou des services écosystémiques affiliés. Cette étude vise à diagnostiquer et analyser la perception des populations sur la fragmentation de la Forêt Classée de Mbao (FCM), au Sénégal. Pour clarifier cette fragmentation paysagère, une étude paysagère a été menée à partir d'une image Landsat de 2024, acquise à partir du capteur OLI. Une classification supervisée a été effectuée, et sa fiabilité a été vérifiée. La perception paysagère a été mise en évidence et corrélée avec le calcul de l'indice de
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Selianinau, Mikhail. "Podejście probabilistyczne do klasyfikacji cyfrowych obrazów twarzy." Prace Naukowe Akademii im. Jana Długosza w Częstochowie. Technika, Informatyka, Inżynieria Bezpieczeństwa 6 (2018): 563–74. http://dx.doi.org/10.16926/tiib.2018.06.40.

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Subhankar, Manna, and G. Malathi. "PERFORMANCE ANALYSIS OF CLASSIFICATION ALGORITHM ON DIABETES HEALTHCARE DATASET." International Journal of Research - Granthaalayah 5, no. 8 (2017): 260–66. https://doi.org/10.5281/zenodo.890581.

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Healthcare industry collects huge amount of unclassified data every day. For an effective diagnosis and decision making, we need to discover hidden data patterns. An instance of such dataset is associated with a group of metabolic diseases that vary greatly in their range of attributes. The objective of this paper is to classify the diabetic dataset using classification techniques like Naive Bayes, ID3 and k means classification. The secondary objective is to study the performance of various classification algorithms used in this work. We propose to implement the classification algorithm using
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Gouiouez, Mounir. "Probabilistic Graphical Model based on BablNet for Arabic Text Classification." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (2020): 1241–50. http://dx.doi.org/10.5373/jardcs/v12sp7/20202224.

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Yang, Na, and Yongtao Zhang. "A Gaussian Process Classification and Target Recognition Algorithm for SAR Images." Scientific Programming 2022 (January 20, 2022): 1–10. http://dx.doi.org/10.1155/2022/9212856.

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Abstract (sommario):
Synthetic aperture Radar (SAR) uses the relative movement of the Radar and the target to pick up echoes of the detected area and image it. In contrast to optical imaging, SAR imaging systems are not affected by weather and time and can detect targets in harsh conditions. Therefore, the SAR image has important application value in military and civilian purposes. This paper introduces the classification of Gaussian process. Gaussian process classification is a probabilistic classification algorithm based on Bass frame. This is a complete probability expression. Based on Gaussian process and SAR
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Tesi sul tema "Classification probabiliste"

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Ambroise, Christophe. "Approche probabiliste en classification automatique et contraintes de voisinage." Compiègne, 1996. http://www.theses.fr/1996COMPD917.

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Ce travail propose de nouveaux algorithmes de classification pour résoudre des problèmes d'analyse de données où des contraintes naturelles apparaissent : respect d'une topologie (cartes de Kohonen), données spatiales. Les mélanges finis de lois gaussiennes et l'estimation de paramètres par l'algorithme EM constituent le cadre de ce mémoire. Le modèle des cartes topologiques de Kohonen introduisant la notion de contrainte, nous nous sommes intéressés à montrer les liens qui existent entre cette approche et les modèles de mélanges. Cette recherche a abouti au développement de variantes de l'alg
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Bzioui, Mohamed. "Classification croisée et modèle." Compiègne, 1999. http://www.theses.fr/1999COMP1226.

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Dans le cadre des tableaux de contingences, des tableaux binaires et des tableaux de mesures, la classification croisée est basée sur un critère métrique sans faire référence à un modèle probabiliste. Dans ce travail, nous proposons un modèle de mélange croisé afin d'apporter un éclairage aux critères métriques existants, d'en développer d'autres et de proposer une solution au problème des données manquantes. Cette étude est réalisée sous deux approches : approche classification et approche estimation. En outre, nous étudions l'influence du choix entre les deux hypothèses : proportions des com
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Touzani, Abderrahmane. "Classification automatique par détection des contours des modes des fonctions de densité de probabilité multivariables et étiquetage probabiliste." Grenoble 2 : ANRT, 1987. http://catalogue.bnf.fr/ark:/12148/cb37610380w.

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Aznag, Mustapha. "Modélisation thématique probabiliste des services web." Thesis, Aix-Marseille, 2015. http://www.theses.fr/2015AIXM4028.

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Les travaux sur la gestion des services web utilisent généralement des techniques du domaine de la recherche d'information, de l'extraction de données et de l'analyse linguistique. Alternativement, nous assistons à l'émergence de la modélisation thématique probabiliste utilisée initialement pour l'extraction de thèmes d'un corpus de documents. La contribution de cette thèse se situe à la frontière de la modélisation thématique et des services web. L'objectif principal de cette thèse est d'étudier et de proposer des algorithmes probabilistes pour modéliser la structure thématique des services w
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Touzani, Abderrahmane. "Classification automatique par détection des contours des modes des fonctions de densité de probabilité multivariables et étiquetage probabiliste." Lille 1, 1987. http://www.theses.fr/1987LIL10058.

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Présentation des méthodes non paramétriques permettant de connaître la valeur de la fonction de densité en chacun des points d'une discrétisation de l'espace en hypercubes élémentaires. L'estimateur est mis en forme grâce à l'introduction d'un filtre de type médian multidimensionnel. Deux opérateurs différentiels sont introduits et appliqués aux fonctions de densité estimées et filtrées. Un algorithme d'extraction de contour séquentiel permet d'exploiter la réponse des opérateurs différentiels pour identifier les contours des modes. L'intérêt de l'approche présentée est démontre pour les probl
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Bassolet, Cyr Gabin. "Approches connexionnistes du classement en Osiris : vers un classement probabiliste." Université Joseph Fourier (Grenoble), 1998. http://www.theses.fr/1998GRE10086.

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Le classement d'instance est une fonction importante des systèmes de représentation de connaissances. Il est présent dans les systèmes de représentation de connaissances centrée objet sous le nom de classification d'objet, dans les logiques terminologiques comme un cas particulier de la classification de concepts, et, de manière implicite, dans les systèmes à base de règles, où les faits inférés peuvent être interprétés comme l'appartenance à une classe. Nous étudions le classement d'instance en Osiris, un système de représentation de connaissances centrée objets où la notion de vue jouent un
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PRICE, DAVID. "Classification probabiliste par reseaux de neurones ; application a la reconnaissance de l'ecriture manuscrite." Paris 6, 1996. http://www.theses.fr/1996PA066344.

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Cette these decrit une contribution a la realisation d'un systeme automatique de reconnaissance des montants litteraux de cheques, en ecriture manuscrite cursive. Parmi les nombreuses phases de traitement de l'information necessaires pour leur reconnaissance, nous nous interessons uniquement, dans ce travail, a la phase de classification: notre objectif est de fournir, pour chaque caractere, une liste d'identifications possibles, par ordre de vraisemblance decroissante. Cette liste est ensuite traitee par un systeme superviseur qui, a partir de plusieurs informations differentes, prend une dec
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Dong, Yuan. "Modélisation probabiliste de classifieurs d’ensemble pour des problèmes à deux classes." Thesis, Troyes, 2013. http://www.theses.fr/2013TROY0013/document.

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Abstract (sommario):
L'objectif de cette thèse est d'améliorer ou de préserver les performances d'un système décisionnel quand l’environnement peut impacter certains attributs de l'espace de représentation à un instant donné ou en fonction de la position géographique de l’observation. S'inspirant des méthodes d'ensemble, notre approche a consisté à prendre les décisions dans des sous-espaces de représentation résultant de projections de l'espace initial, espérant ainsi travailler dans des sous-espaces non impactés. La décision finale est alors prise par fusion des décisions individuelles. Dans ce contexte, trois m
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Mselati, Benoît. "Classification et représentation probabiliste des solutions positives de delta u = u2 dans un domaine." Paris 6, 2002. http://www.theses.fr/2002PA066496.

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Charon, Clara. "Classification probabiliste pour la prédiction et l'explication d'événements de santé défavorables et évitables en EHPAD." Electronic Thesis or Diss., Sorbonne université, 2024. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2024SORUS200.pdf.

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L'EHPAD, établissement d'hébergement pour personnes âgées dépendantes, constitue une option à laquelle a recours une population nombreuse et croissante, lorsque pour diverses raisons, et notamment de santé, il n'est plus possible de vivre à domicile.Avec le développement des nouvelles technologies informatiques dans le domaine de la santé, un nombre croissant d'établissements de santé sont équipés de systèmes d'information regroupant les données administratives et médicales des patients ainsi que des informations sur les soins qui leur sont prodigués.Parmi ces systèmes, les dossiers médicaux é
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Libri sul tema "Classification probabiliste"

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Hack, Henri Robert George Kenneth. Slope stability probability classification: SSPC = Helling stabiliteit classificatie : SSPC. International Institute for Aerospace Survey and Earth Sciences, 1996.

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Spray, Judith A. Multiple-category classification using a sequential probability ratio test. American College Testing Program, 1993.

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Spray, Judith A. Multiple-category classification using a sequential probability ratio test. American College Testing Program, 1993.

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Spray, Judith A. Multiple-category classification using a sequential probability ratio test. American College Testing Program, 1993.

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1967-, Meira Wagner, ed. Demand-driven associative classification. Springer, 2011.

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Lin, Chuan-Ju. Effects of item-selection criteria on classification testing with the sequential probability ratio test. ACT, Inc., 2000.

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Lin, Chuan-Ju. Effects of item-selection criteria on classification testing with the sequential probability ratio test. ACT, Inc., 2000.

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Lin, Chuan-Ju. Effects of item-selection criteria on classification testing with the sequential probability ratio test. ACT, Inc., 2000.

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Baram, Yoram. Estimation and classification by sigmoids based on mutual information. National Aeronautics and Space Administration, 1994.

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Classification Group of SIS. Meeting. Classification and data analysis: Theory and application : proceedings of the biannual meeting of the Classification Group of Societa Italia di Statistica (SIS), Pescara, July 3-4, 1997. Edited by Vichi Maurizio 1959- and Opitz Otto. Springer, 1999.

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Capitoli di libri sul tema "Classification probabiliste"

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Gower, John C., and Gavin J. S. Ross. "Non-probabilistic Classification." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72253-0_3.

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Bock, Hans H. "Probability Models for Convex Clusters." In Classification and Knowledge Organization. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-59051-1_1.

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Fuhr, Norbert. "Representations, Models and Abstractions in Probabilistic Information Retrieval." In Information and Classification. Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-50974-2_26.

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Pompe, Uroš, and Igor Kononenko. "Probabilistic first-order classification." In Inductive Logic Programming. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3540635149_52.

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Bernardo, José M. "Bayesian Linear Probabilistic Classification." In Statistical Decision Theory and Related Topics IV. Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4613-8768-8_19.

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Vovk, Vladimir, Alexander Gammerman, and Glenn Shafer. "Probabilistic Classification: Venn Predictors." In Algorithmic Learning in a Random World. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06649-8_6.

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Aggarwal, Charu C. "Classification: A Probabilistic View." In Probability and Statistics for Machine Learning. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53282-5_8.

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Bock, Hans H. "Probabilistic Aspects in Classification." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer Japan, 1998. http://dx.doi.org/10.1007/978-4-431-65950-1_1.

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Dellmann, F. "Optimization in Probabilistic Logic for Decision Support Systems." In Classification and Knowledge Organization. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-59051-1_27.

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Guo, Gongde, Hui Wang, David Bell, and Zhining Liao. "Contextual Probability-Based Classification." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30464-7_25.

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Atti di convegni sul tema "Classification probabiliste"

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Kessler, Sylvia, and Ivan Lasa. "Study on Probabilistic Service Life of Reinforced Concrete Bridges Structures in Florida Exposed to Chlorides." In CORROSION 2019. NACE International, 2019. https://doi.org/10.5006/c2019-13117.

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Abstract The durability design of new structures at Florida Department of Transportation (DOT) is currently performed based on environmental classification of structure's sites. Bridge substructure and superstructure environments can be classified as slightly aggressive, moderately aggressive or extremely aggressive according to the FDOT Structures Design Guidelines. Each classification requires prerequisites regarding concrete composition and concrete cover. The limitation of this durability design approach is that the actual achievable service life remains unknown. However, full-probabilisti
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Gorguluarslan, Recep M., and Seung-Kyum Choi. "Predicting Reliability of Structural Systems Using Classification Method." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-13323.

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This research examines classification approaches for estimating the reliability of structural systems. To validate the accuracy and efficiency of the classification methods, a practical engineering problem; namely, a spider assembly of a washing machine, has been considered. For the spider assembly, fatigue life test, finite element analysis, physical experimentation, and a classification processes are conducted in order to establish the analytical certification of its current design. Specifically, the finite element analysis and fatigue life analysis are performed and their results are valida
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Horte, T., R. Skjong, P. Friis-Hansen, A. P. Teixeira, and F. Viejo de Francisco. "Probabilistic Methods Applied To Structural Design And Rule Development." In Developments in Classification & International Regulation 2007. RINA, 2007. http://dx.doi.org/10.3940/rina.dcir.2007.07.

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Bailer-Jones, Coryn A. L., Kester W. Smith, and Coryn A. L. Bailer-Jones. "Finding rare objects and building pure samples: Probabilistic quasar classification with Gaia." In CLASSIFICATION AND DISCOVERY IN LARGE ASTRONOMICAL SURVEYS: Proceedings of the International Conference: “Classification and Discovery in Large Astronomical Surveys”. AIP, 2008. http://dx.doi.org/10.1063/1.3059079.

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Cardelli, Luca, Marta Kwiatkowska, Luca Laurenti, Nicola Paoletti, Andrea Patane, and Matthew Wicker. "Statistical Guarantees for the Robustness of Bayesian Neural Networks." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/789.

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We introduce a probabilistic robustness measure for Bayesian Neural Networks (BNNs), defined as the probability that, given a test point, there exists a point within a bounded set such that the BNN prediction differs between the two. Such a measure can be used, for instance, to quantify the probability of the existence of adversarial examples. Building on statistical verification techniques for probabilistic models, we develop a framework that allows us to estimate probabilistic robustness for a BNN with statistical guarantees, i.e., with a priori error and confidence bounds. We provide experi
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Pereira, Rafael S., and Fabio Porto. "Deep Learning Application for Plant Classification on Unbalanced Training Set." In XIII Brazilian e-Science Workshop. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/bresci.2019.10023.

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Deep learning models expect a reasonable amount of training instances to improve prediction quality. Moreover, in classification problems, the occurrence of an unbalanced distribution may lead to a biased model. In this paper, we investigate the problem of species classification from plant images, where some species have very few image samples. We explore reduced versions of imagenet Neural Network winners architecture to filter the space of candidate matches, under a target accuracy level. We show through experimental results using real unbalanced plant image datasets that our approach can le
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Pereira, Rafael S., and Fabio Porto. "Deep Learning Application for Plant Classification on Unbalanced Training Set." In XIII Brazilian e-Science Workshop. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/bresci.2019.6304.

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Abstract (sommario):
Deep learning models expect a reasonable amount of training in- stances to improve prediction quality. Moreover, in classification problems, the occurrence of an unbalanced distribution may lead to a biased model. In this paper, we investigate the problem of species classification from plant images, where some species have very few image samples. We explore reduced versions of imagenet Neural Network winners architecture to filter the space of candi- date matches, under a target accuracy level. We show through experimental results using real unbalanced plant image datasets that our approach ca
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Sun, Haotian. "Application of Gaussian Process Classification to the Integrity Management of Energy Pipelines." In 2024 15th International Pipeline Conference. American Society of Mechanical Engineers, 2024. https://doi.org/10.1115/ipc2024-134103.

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Abstract Leveraging algorithms that autonomously evolve with data, machine learning has become integral and been extensively applied to the pipeline integrity management practice to create accurate predictive models, whose outputs can either be numerical or categorical. The latter corresponds to the classification analysis, whose models typically yield deterministic outputs, categorizing data points as of either the positive or negative class. However, practical engineering scenarios may demand a more nuanced approach. Additionally, the conventional “black-box” nature of many machine learning
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Albini, Emanuele, Antonio Rago, Pietro Baroni, and Francesca Toni. "Relation-Based Counterfactual Explanations for Bayesian Network Classifiers." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/63.

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We propose a general method for generating counterfactual explanations (CFXs) for a range of Bayesian Network Classifiers (BCs), e.g. single- or multi-label, binary or multidimensional. We focus on explanations built from relations of (critical and potential) influence between variables, indicating the reasons for classifications, rather than any probabilistic information. We show by means of a theoretical analysis of CFXs’ properties that they serve the purpose of indicating (potentially) pivotal factors in the classification process, whose absence would give rise to different classifications
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Косян, Рубен, Ruben Kosyan, Viacheslav Krylenko, and Viacheslav Krylenko. "DEVELOPMENT OF THE BASIC CRITERIA FOR RUSSIAN COASTS TYPIFICATION." In Managing risks to coastal regions and communities in a changing world. Academus Publishing, 2017. http://dx.doi.org/10.31519/conferencearticle_5b1b94080e4924.02334863.

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Abstract (sommario):
There are many types of coasts classifications that indicate main coastal features. As a rule, the "static" state of the coasts is considered regardless of their evolutionary features and ways to further transformation. Since the most part of the coastal zone studies aimed at ensuring of economic activity, it is clear that the classification of coast types should indicate total information required by the users. Accordingly, the coast classification should include the criterion, characterizing as dynamic features of the coast and the conditions and opportunities of economic activity. The coast
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Rapporti di organizzazioni sul tema "Classification probabiliste"

1

Zeitouni, Ofer, and Sanjeev R. Kulkarni. A General Classification Rule for Probability Measures. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada455893.

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2

Zio, Enrico, and Nicola Pedroni. Uncertainty characterization in risk analysis for decision-making practice. Fondation pour une culture de sécurité industrielle, 2012. http://dx.doi.org/10.57071/155chr.

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This document provides an overview of sources of uncertainty in probabilistic risk analysis. For each phase of the risk analysis process (system modeling, hazard identification, estimation of the probability and consequences of accident sequences, risk evaluation), the authors describe and classify the types of uncertainty that can arise. The document provides: a description of the risk assessment process, as used in hazardous industries such as nuclear power and offshore oil and gas extraction; a classification of sources of uncertainty (both epistemic and aleatory) and a description of techn
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3

SOHN, HOON, DAVID W. ALLEN, KEITH WORDEN, and CHARLES R. FARRAR. STATISTICAL DAMAGE CLASSIFICATION USING SEQUENTIAL PROBABILITY RATIO TESTS. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/808089.

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4

Pouliot, D., R. Latifovic, and W. Parkinson. Influence of sample distribution and prior probability adjustment on land cover classification. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2016. http://dx.doi.org/10.4095/297517.

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5

de Luis, Mercedes, Emilio Rodríguez, and Diego Torres. Machine learning applied to active fixed-income portfolio management: a Lasso logit approach. Banco de España, 2023. http://dx.doi.org/10.53479/33560.

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Abstract (sommario):
The use of quantitative methods constitutes a standard component of the institutional investors’ portfolio management toolkit. In the last decade, several empirical studies have employed probabilistic or classification models to predict stock market excess returns, model bond ratings and default probabilities, as well as to forecast yield curves. To the authors’ knowledge, little research exists into their application to active fixed-income management. This paper contributes to filling this gap by comparing a machine learning algorithm, the Lasso logit regression, with a passive (buy-and-hold)
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6

de Dieu Niyigena, Jean, Innocent Ngaruye, Joseph Nzabanita, and Martin Singull. Approximation of misclassification probabilities using quadratic classifier for repeated measurements with known covariance matrices. Linköping University Electronic Press, 2024. http://dx.doi.org/10.3384/lith-mat-r-2024-02.

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Quadratic discriminant analysis is a well-established supervised classification method, which extends the linear the linear discriminant analysis by relaxing the assumption of equal variances across classes. In this study, quadratic discriminant analysis is used to develop a quadratic classification rule based on repeated measurements. We employ a bilinear regression model to assign new observations to predefined populations and approximate the misclassification probability. Through weighted estimators, we estimate unknown mean parameters and derive moments of the quadratic classifier. We then
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7

Eckert, Richard. PR-186-184509-R01 Guideline for Erosional Velocity. Pipeline Research Council International, Inc. (PRCI), 2020. http://dx.doi.org/10.55274/r0011655.

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A guideline to determine erosional velocity limits for liquid hydrocarbon transmission pipelines was developed based on a multi-analytical probabilistic approach that integrated results from two erosional models: DNV GL RP-O501 and University of Tulsa SPPS v5.3. The guideline uses a simple classification tree model as first approach to provide conservative erosional velocities with a minimum amount of input data. The guideline also presents an alternative probabilistic approach for determining erosional velocities when the classification tree cannot be used, e.g., when there is too much data u
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8

Bragdon, Sophia, Vuong Truong, and Jay Clausen. Environmentally informed buried object recognition. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45902.

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Abstract (sommario):
The ability to detect and classify buried objects using thermal infrared imaging is affected by the environmental conditions at the time of imaging, which leads to an inconsistent probability of detection. For example, periods of dense overcast or recent precipitation events result in the suppression of the soil temperature difference between the buried object and soil, thus preventing detection. This work introduces an environmentally informed framework to reduce the false alarm rate in the classification of regions of interest (ROIs) in thermal IR images containing buried objects. Using a da
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9

Lee, W. S., Victor Alchanatis, and Asher Levi. Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7598158.bard.

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Abstract (sommario):
Original objectives and revisions – The original overall objective was to develop, test and validate a prototype yield mapping system for unit area to increase yield and profit for tree crops. Specific objectives were: (1) to develop a yield mapping system for a static situation, using hyperspectral and thermal imaging independently, (2) to integrate hyperspectral and thermal imaging for improved yield estimation by combining thermal images with hyperspectral images to improve fruit detection, and (3) to expand the system to a mobile platform for a stop-measure- and-go situation. There were no
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10

Burns, Malcom, and Gavin Nixon. Literature review on analytical methods for the detection of precision bred products. Food Standards Agency, 2023. http://dx.doi.org/10.46756/sci.fsa.ney927.

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Abstract (sommario):
The Genetic Technology (Precision Breeding) Act (England) aims to develop a science-based process for the regulation and authorisation of precision bred organisms (PBOs). PBOs are created by genetic technologies but exhibit changes which could have occurred through traditional processes. This current review, commissioned by the Food Standards Agency (FSA), aims to clarify existing terminologies, explore viable methods for the detection, identification, and quantification of products of precision breeding techniques, address and identify potential solutions to the analytical challenges presente
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