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Relevant bibliographies by topics / Discriminative classifier

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Academic literature on the topic 'Discriminative classifier'

Author: Grafiati

Published: 11 March 2023

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Dissertations / Theses on the topic "Discriminative classifier"

1

Masip, Rodó David. "Face Classification Using Discriminative Features and Classifier Combination." Doctoral thesis, Universitat Autònoma de Barcelona, 2005. http://hdl.handle.net/10803/3051.

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A mesura que la tecnologia evoluciona, apareixen noves aplicacions en el mon de la classificació facial. En el reconeixement de patrons, normalment veiem les cares com a punts en un espai de alta dimensionalitat definit pels valors dels seus pixels. Aquesta aproximació pateix diversos problemes: el fenomen de la "la maledicció de la dimensionalitat", la presència d'oclusions parcials o canvis locals en la il·luminació. Tradicionalment, només les característiques internes de les imatges facials s'han utilitzat per a classificar, on normalment es fa una extracció de característiques. Les tècniq

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2

Georgatzis, Konstantinos. "Dynamical probabilistic graphical models applied to physiological condition monitoring." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28838.

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Intensive Care Units (ICUs) host patients in critical condition who are being monitored by sensors which measure their vital signs. These vital signs carry information about a patient’s physiology and can have a very rich structure at fine resolution levels. The task of analysing these biosignals for the purposes of monitoring a patient’s physiology is referred to as physiological condition monitoring. Physiological condition monitoring of patients in ICUs is of critical importance as their health is subject to a number of events of interest. For the purposes of this thesis, the overall task o

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3

Klautau, Aldebaro. "Speech recognition using discriminative classifiers /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2003. http://wwwlib.umi.com/cr/ucsd/fullcit?p3091208.

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4

Xue, Jinghao. "Aspects of generative and discriminative classifiers." Thesis, Connect to e-thesis, 2008. http://theses.gla.ac.uk/272/.

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Thesis (Ph.D.) - University of Glasgow, 2008.<br>Ph.D. thesis submitted to the Department of Statistics, Faculty of Information and Mathematical Sciences, University of Glasgow, 2008. Includes bibliographical references. Print version also available.

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5

Pernot, Etienne. "Choix d'un classifieur en discrimination." Paris 9, 1994. https://portail.bu.dauphine.fr/fileviewer/index.php?doc=1994PA090014.

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Dans cette thèse, nous nous posons le problème de la détermination du classifieur le plus adapté à résoudre un problème donné de discrimination. Le choix du classifieur est déjà guidé par des contraintes opérationnelles, mais au-delà de ces contraintes, et après que le classifieur a été configuré grâce à une base d'apprentissage, c'est le taux de généralisation du classifieur (ou taux de réussite) qui est le critère caractérisant sa performance. Ce taux, généralement inconnu, est estimé à l'aide d'une base de généralisation. Cette estimation dépend donc du problème de discrimination étudié, du

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6

Katz, Marcel [Verfasser]. "Discriminative classifiers for speaker Recognition / Marcel Katz." Saarbrücken : Südwestdeutscher Verlag für Hochschulschriften, 2009. http://www.vdm-verlag.de.

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7

ABDALLAH, HICHAM. "Application de l'analyse relationnelle pour classifier descripteurs et modalites en mode discrimination." Paris 6, 1996. http://www.theses.fr/1996PA066001.

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Dans le processus presente dans la these, quatre niveaux fondamentaux ont ete a l'origine de notre travail: 1 la classification de l'espace des descripteurs ou des variables pour reduire et resserrer cet espace: en introduisant de nouvelles structures (par des criteres d'associations connus) permettant de donner des bornes ou seuils au dela desquels on considere les deux descripteurs ou variables sont ressemblants. Un point important ici est la possibilite de tout expliciter en fonction du rand ou du chi-deux. Cette etude permet de voir apparaitre la notion de severite pour chaque critere, qui

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8

Dastile, Xolani Collen. "Improved tree species discrimination at leaf level with hyperspectral data combining binary classifiers." Thesis, Rhodes University, 2011. http://hdl.handle.net/10962/d1002807.

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The purpose of the present thesis is to show that hyperspectral data can be used for discrimination between different tree species. The data set used in this study contains the hyperspectral measurements of leaves of seven savannah tree species. The data is high-dimensional and shows large within-class variability combined with small between-class variability which makes discrimination between the classes challenging. We employ two classification methods: G-nearest neighbour and feed-forward neural networks. For both methods, direct 7-class prediction results in high misclassification rates. H

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9

Rüther, Johannes. "Navigating Deep Classifiers : A Geometric Study Of Connections Between Adversarial Examples And Discriminative Features In Deep Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291775.

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Although deep networks are powerful and effective in numerous applications, their high vulnerability to adversarial perturbations remains a critical limitation in domains such as security, personalized medicine or autonomous systems. While the sensitivity to adversarial perturbations is generally viewed as a bug of deep classifiers, recent research suggests that they are actually a manifestation of non-robust features that deep classifiers exploit for predictive accuracy. In this work, we therefore systematically compute and analyze these perturbations to understand how they relate to discrimi

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10

Musayeva, Khadija. "Generalization Performance of Margin Multi-category Classifiers." Thesis, Université de Lorraine, 2019. http://www.theses.fr/2019LORR0096/document.

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Cette thèse porte sur la théorie de la discrimination multi-classe à marge. Elle a pour cadre la théorie statistique de l’apprentissage de Vapnik et Chervonenkis. L’objectif est d’établir des bornes de généralisation possédant une dépendances explicite au nombre C de catégories, à la taille m de l’échantillon et au paramètre de marge gamma, lorsque la fonction de perte considérée est une fonction de perte à marge possédant la propriété d’être lipschitzienne. La borne de généralisation repose sur la performance empirique du classifieur ainsi que sur sa "capacité". Dans cette thèse, les mesures

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