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Dissertations / Theses on the topic 'Supervised classifier'

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Published: 4 June 2021

Last updated: 26 July 2025

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1

Breland, Adrienne E. "A supervised strain classifier." abstract and full text PDF (free order & download UNR users only), 2008. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1453199.

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Gu, Shenkai. "Multi-objective and semi-supervised heterogeneous classifier ensembles." Thesis, University of Surrey, 2017. http://epubs.surrey.ac.uk/813278/.

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In the recent years, many applications in machine learning involve an increasingly large number of features and samples, which poses new challenges to many learning algorithms. To address these challenges, ensemble learning methods, which uses multiple base learners, have been proposed to achieve better predictive performance. This thesis covers a range of topics in ensemble classification, including multi-objective and semi-supervised heterogeneous classier ensembles. We first present an empirical study on heterogeneous classifier ensembles, which confirms that heterogeneous ensembles outperf

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Scalise, Damiano <1991&gt. "Global biome shifts under climate change scenarios: a supervised classifier modeling approach." Master's Degree Thesis, Università Ca' Foscari Venezia, 2018. http://hdl.handle.net/10579/12480.

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Quantificare il potenziale di impatto dei diversi scenari di cambiamento climatico in relazione agli areali di distribuzione dei biomi terrestri (archetipi di comunità ecologiche prevalenti su larga scala) è un obiettivo importante sia per motivi di ricerca, sia nella sfera della sensibilizzazione pubblica. Negli ultimi due decenni diversi approcci sono emersi, ma, per via della grande varietà di metologie, i risultati non sono propriamente comparabili in termini quantitativi. Inoltre, fino ad oggi, nessuno studio ha potuto fornire un approccio coerentemente validato per la realizzazione di ma

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Watkins, Andrew B. "AIRS: a resource limited artificial immune classifier." Master's thesis, Mississippi State : Mississippi State University, 2001. http://library.msstate.edu/etd/show.asp?etd=etd-11052001-102048.

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Lindholm, Alexander. "A study about fraud detection and the implementation of SUSPECT - Supervised and UnSuPervised Erlang Classifier Tool." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-222774.

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Fraud detection is a game of cat and mouse between companies and people trying to commit fraud. Most of the work within the area is not published due to several reasons. One of the reasons is that if a company publishes how their system works, the public will know how to evade detection. This paper describes the implementation of a proof-of-concept fraud detection system. The prototype named SUSPECT uses two different methods for fraud detection. The first one being a supervised classifier in form of an artificial neural network and the second one being an unsupervised classifier in the form

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Shafi, Kamran Information Technology &amp Electrical Engineering Australian Defence Force Academy UNSW. "An online and adaptive signature-based approach for intrusion detection using learning classifier systems." Awarded by:University of New South Wales - Australian Defence Force Academy, 2008. http://handle.unsw.edu.au/1959.4/38991.

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This thesis presents the case of dynamically and adaptively learning signatures for network intrusion detection using genetic based machine learning techniques. The two major criticisms of the signature based intrusion detection systems are their i) reliance on domain experts to handcraft intrusion signatures and ii) inability to detect previously unknown attacks or the attacks for which no signatures are available at the time. In this thesis, we present a biologically-inspired computational approach to address these two issues. This is done by adaptively learning maximally general rules, whic

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Bouzouita-Bayoudh, Inès. "Etude et extraction des règles associatives de classification en classification supervisée." Thesis, Montpellier 2, 2012. http://www.theses.fr/2012MON20217.

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Dans le cadre de cette thèse, notre intérêt se porte sur la précision de la classification et l'optimalité du parcours de l'espace de recherche. L'objectif recherché est d'améliorer la précision de classification en étudiant les différents types de règles et de réduire l'espace de recherche des règles. Nous avons proposé une approche de classification IGARC permettant de générer un classifieur formé d'une base de règles de classification génériques permettant de mieux classer les nouveaux objets grâce à la flexibilité de petites prémisses caractérisant ces règles. De plus cette approche manipu

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Duan, Haoyang. "Applying Supervised Learning Algorithms and a New Feature Selection Method to Predict Coronary Artery Disease." Thèse, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31113.

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From a fresh data science perspective, this thesis discusses the prediction of coronary artery disease based on Single-Nucleotide Polymorphisms (SNPs) from the Ontario Heart Genomics Study (OHGS). First, the thesis explains the k-Nearest Neighbour (k-NN) and Random Forest learning algorithms, and includes a complete proof that k-NN is universally consistent in finite dimensional normed vector spaces. Second, the thesis introduces two dimensionality reduction techniques: Random Projections and a new method termed Mass Transportation Distance (MTD) Feature Selection. Then, this thesis compares t

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Dam, Hai Huong Information Technology &amp Electrical Engineering Australian Defence Force Academy UNSW. "A scalable evolutionary learning classifier system for knowledge discovery in stream data mining." Awarded by:University of New South Wales - Australian Defence Force Academy, 2008. http://handle.unsw.edu.au/1959.4/38865.

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Data mining (DM) is the process of finding patterns and relationships in databases. The breakthrough in computer technologies triggered a massive growth in data collected and maintained by organisations. In many applications, these data arrive continuously in large volumes as a sequence of instances known as a data stream. Mining these data is known as stream data mining. Due to the large amount of data arriving in a data stream, each record is normally expected to be processed only once. Moreover, this process can be carried out on different sites in the organisation simultaneously making the

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Alves, Matheus. "Social training : aprendizado semi supervisionado utilizando funções de escolha social." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/169887.

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Dada a grande quantidade de dados gerados atualmente, apenas uma pequena porção dos mesmos pode ser rotulada manualmente por especialistas humanos. Isso é um desafio comum para aplicações de aprendizagem de máquina. Aprendizado semi-supervisionado aborda este problema através da manipulação dos dados não rotulados juntamente aos dados rotulados. Entretanto, se apenas uma quantidade limitada de exemplos rotulados está disponível, o desempenho da tarefa de aprendizagem de máquina (e.g., classificação) pode ser não satisfatória. Diversas soluções abordam este problema através do uso de uma ensemb

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Mauricio-Sanchez, David, Andrade Lopes Alneu de, and higuihara Juarez Pedro Nelson. "Approaches based on tree-structures classifiers to protein fold prediction." Institute of Electrical and Electronics Engineers Inc, 2017. http://hdl.handle.net/10757/622536.

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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.<br>Protein fold recognition is an important task in the biological area. Different machine learning methods such as multiclass classifiers, one-vs-all and ensemble nested dichotomies were applied to this task and, in most of the cases, multiclass approaches were used. In this paper, we compare classifiers organized in tree structures to classify folds. We used a benchmark dataset containing 125 features to predict folds, comparing different superv

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Bel, Haj Ali Wafa. "Minimisation de fonctions de perte calibrée pour la classification des images." Phd thesis, Université Nice Sophia Antipolis, 2013. http://tel.archives-ouvertes.fr/tel-00934062.

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La classification des images est aujourd'hui un défi d'une grande ampleur puisque ça concerne d'un côté les millions voir des milliards d'images qui se trouvent partout sur le web et d'autre part des images pour des applications temps réel critiques. Cette classification fait appel en général à des méthodes d'apprentissage et à des classifieurs qui doivent répondre à la fois à la précision ainsi qu'à la rapidité. Ces problèmes d'apprentissage touchent aujourd'hui un grand nombre de domaines d'applications: à savoir, le web (profiling, ciblage, réseaux sociaux, moteurs de recherche), les "Big D

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Eke, Samuel. "Stratégie d'évaluation de l'état des transformateurs : esquisse de solutions pour la gestion intégrée des transformateurs vieillissants." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSEC013/document.

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Cette thèse de doctorat traite des méthodes d’évaluation de l’état des transformateurs de puissance à huile. Elle apporte une approche particulière de mise en oeuvre des méthodes de classification dans la fouille de données. Elle propose une stratégie qui met en oeuvre deux nouveaux indicateurs de santé de l’huile construit à partir d’un système neuro flou ANFIS (Adaptative Neuro-Fuzzy Inference System) et un classifieur ou prédicteur de défaut construit à partir des méthodes de classification supervisée, notamment le classifieur Bayésien naïf. Un organigramme simple et efficace d’évaluation d

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Kola, Lokesh, and Vigneshwar Muriki. "A Comparison on Supervised and Semi-Supervised Machine Learning Classifiers for Diabetes Prediction." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21816.

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Background: The main cause of diabetes is due to high sugar levels in the blood. There is no permanent cure for diabetes. However, it can be prevented by early diagnosis. In recent years, the hype for Machine Learning is increasing in disease prediction especially during COVID-19 times. In the present scenario, it is difficult for patients to visit doctors. A possible framework is provided using Machine Learning which can detect diabetes at early stages. Objectives: This thesis aims to identify the critical features that impact gestational (Type-3) diabetes and experiments are performed to ide

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Lay, Nathan. "Supervised aggregation of classifiers using artificial prediction markets." Tallahassee, Florida : Florida State University, 2009. http://etd.lib.fsu.edu/theses/available/etd-11082009-145713/.

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Thesis (M.S.)--Florida State University, 2009.<br>Advisor: Adrian Barbu, Florida State University, College of Arts and Sciences, Dept. of Scientific Computing. Title and description from dissertation home page (viewed on May 12, 2010). Document formatted into pages; contains vii, 46 pages. Includes bibliographical references.

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Lavesson, Niklas. "Evaluation and Analysis of Supervised Learning Algorithms and Classifiers." Licentiate thesis, Karlskrona : Blekinge Institute of Technology, 2006. http://www.bth.se/fou/Forskinfo.nsf/allfirst2/c655a0b1f9f88d16c125714c00355e5d?OpenDocument.

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Pauw, Theo. "Assessment of SPOT 5 and ERS-2 OBIA for mapping wetlands." Thesis, Stellenbosch : Stellenbosch University, 2012. http://hdl.handle.net/10019.1/71906.

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Thesis (MSc)--Stellenbosch University, 2012.<br>ENGLISH ABSTRACT: This research considered the automated remote sensing-based classification of wetland extent within the Nuwejaars and Heuningnes River systems on the Agulhas Plain. The classification process was based on meaningful image objects created through image segmentation rather than on single pixels. An expert system classifier was compared to a nearest-neighbour supervised classifier, and one multispectral (SPOT 5) image (dry season) and two C-band, VV-polarisation synthetic aperture radar (SAR: ERS-2) images (dry and wet season) were

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Na, Li. "Combination of supervised and unsupervised classifiers based on belief functions." Thesis, Rennes 1, 2020. http://www.theses.fr/2020REN1S041.

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La couverture terrestre se rapporte à la couverture biophysique de la surface terrestre de la Terre, identifiant ainsi la végétation, l’eau, le sol nu ou les surfaces imperméables, etc. L’identification de la couverture terrestre est essentielle pour la planification et la gestion des ressources naturelles (e.g. d développement, protection), la compréhension de la répartition des habitats ainsi que la modélisation des variables environnementales. L’identification des types de couverture terrestre fournit des informations de base pour la production d’autres cartes thématiques et établit une bas

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Mancini, Lorenzo <1989&gt. "Ordinal data supervised classification with Quantile-based and other classifiers." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amsdottorato.unibo.it/8543/1/phd%20thesis_mancini.pdf.

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The aim of this research project is to propose a new method for supervised classification problems where the input features are ordinal. Ordinal data are preponderant in many research fields. They directly arise when the observations fall into separate distinct but ordered categories and they are very common in surveys where answers are listed as Likert scales. Typically, they are coded as equally spaced values and sometimes they are analyzed as numerical values. These choices may not necessarily correspond to the real distribution of the data. The objectives of the study have been accompli

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Faucheux, Cyrille. "Segmentation supervisée d'images texturées par régularisation de graphes." Thesis, Tours, 2013. http://www.theses.fr/2013TOUR4050/document.

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Dans cette thèse, nous nous intéressons à un récent algorithme de segmentation d’images basé sur un processus de régularisation de graphes. L’objectif d’un tel algorithme est de calculer une fonction indicatrice de la segmentation qui satisfait un critère de régularité ainsi qu’un critère d’attache aux données. La particularité de cette approche est de représenter les images à l’aide de graphes de similarité. Ceux-ci permettent d’établir des relations entre des pixels non-adjacents, et ainsi de procéder à un traitement non-local des images. Afin d’en améliorer la précision, nous combinons cet

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Ford, John M. "Pulsar Search Using Supervised Machine Learning." NSUWorks, 2017. http://nsuworks.nova.edu/gscis_etd/1001.

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Pulsars are rapidly rotating neutron stars which emit a strong beam of energy through mechanisms that are not entirely clear to physicists. These very dense stars are used by astrophysicists to study many basic physical phenomena, such as the behavior of plasmas in extremely dense environments, behavior of pulsar-black hole pairs, and tests of general relativity. Many of these tasks require information to answer the scientific questions posed by physicists. In order to provide more pulsars to study, there are several large-scale pulsar surveys underway, which are generating a huge backlog of u

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Dang, Yue. "A Comparative Study of Bagging and Boosting of Supervised and Unsupervised Classifiers For Outliers Detection." Wright State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=wright1502475855457354.

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Van, der Walt Christiaan Maarten. "Data measures that characterise classification problems." Diss., Pretoria : [s.n.], 2008. http://upetd.up.ac.za/thesis/available/etd-08292008-162648/.

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Kamat, Sai Shyamsunder. "Analyzing Radial Basis Function Neural Networks for predicting anomalies in Intrusion Detection Systems." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259187.

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In the 21st century, information is the new currency. With the omnipresence of devices connected to the internet, humanity can instantly avail any information. However, there are certain are cybercrime groups which steal the information. An Intrusion Detection System (IDS) monitors a network for suspicious activities and alerts its owner about an undesired intrusion. These commercial IDS’es react after detecting intrusion attempts. With the cyber attacks becoming increasingly complex, it is expensive to wait for the attacks to happen and respond later. It is crucial for network owners to emplo

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Brody, Samuel. "Closing the gap in WSD : supervised results with unsupervised methods." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/3981.

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Word-Sense Disambiguation (WSD), holds promise for many NLP applications requiring broad-coverage language understanding, such as summarization (Barzilay and Elhadad, 1997) and question answering (Ramakrishnan et al., 2003). Recent studies have also shown that WSD can benefit machine translation (Vickrey et al., 2005) and information retrieval (Stokoe, 2005). Much work has focused on the computational treatment of sense ambiguity, primarily using data-driven methods. The most accurate WSD systems to date are supervised and rely on the availability of sense-labeled training data. This restricti

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Myburgh, Gerhard. "The impact of training set size and feature dimensionality on supervised object-based classification : a comparison of three classifiers." Thesis, Stellenbosch : Stellenbosch University, 2012. http://hdl.handle.net/10019.1/71655.

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Thesis (MSc)--Stellenbosch University, 2012.<br>ENGLISH ABSTRACT: Supervised classifiers are commonly used in remote sensing to extract land cover information. They are, however, limited in their ability to cost-effectively produce sufficiently accurate land cover maps. Various factors affect the accuracy of supervised classifiers. Notably, the number of available training samples is known to significantly influence classifier performance and to obtain a sufficient number of samples is not always practical. The support vector machine (SVM) does perform well with a limited number of traini

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Abou, Latif Firas. "Identification du profil des utilisateurs d’un hypermédia encyclopédique à l’aide de classifieurs basés sur des dissimilarités : création d’un composant d’un système expert pour Hypergéo." Thesis, Rouen, INSA, 2011. http://www.theses.fr/2011ISAM0004/document.

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L’objectif de cette thèse est d’identifier le profil d’utilisateur d’un hypermédia afin de l’adapter. Ceprofil est déterminé en utilisant des algorithmes d’apprentissage supervisé comme le SVM.Le modèle d’utilisateur est l’un des composants essentiels des hypermédias adaptatifs. Une des façons de caractériser ce modèle est d’associer l’utilisateur à un profil. Le Web Usage Mining (WUM)identifie ce profil à l’aide des traces de navigation. Toutefois, ces techniques ne fonctionnent généralement que sur de gros volumes de données. Dans le cadre de volumes de données réduits, nous proposons d’util

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Edmonds, Andrew Nicola. "Time series prediction using supervised learning and tools from chaos theory." Thesis, University of Bedfordshire, 1996. http://hdl.handle.net/10547/582141.

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In this work methods for performing time series prediction on complex real world time series are examined. In particular series exhibiting non-linear or chaotic behaviour are selected for analysis. A range of methodologies based on Takens' embedding theorem are considered and compared with more conventional methods. A novel combination of methods for determining the optimal embedding parameters are employed and tried out with multivariate financial time series data and with a complex series derived from an experiment in biotechnology. The results show that this combination of techniques provid

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Gacquer, David. "Sur l'utilisation active de la diversité dans la construction d'ensembles classifieurs : application à la détection de fumées nocives sur site industriel." Valenciennes, 2009. http://ged.univ-valenciennes.fr/nuxeo/site/esupversions/2a04cf89-c324-43d6-a36b-052aa232f813.

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L'influence de la diversité lors de la construction d'ensembles de classifieurs soulève de nombreuses discussions en Apprentissage Automatique. L’une des méthodes pour construire un ensemble de classifieurs, suit le paradigme « Surproduction et Sélection » et consiste à sélectionner certains membres d’un ensemble de classifieurs selon des critères prédéfinis. Les travaux présentés dans cette thèse ont pour objectif d'étudier le compromis entre la précision et la diversité qui caraCtérisent les ensembles de classifieurs. Nous présentons différents algorithmes d'apprentissage ainsi que les métho

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Perez, Daniel Antonio. "Performance comparison of support vector machine and relevance vector machine classifiers for functional MRI data." Thesis, Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34858.

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Multivariate pattern analysis (MVPA) of fMRI data has been growing in popularity due to its sensitivity to networks of brain activation. It is performed in a predictive modeling framework which is natural for implementing brain state prediction and real-time fMRI applications such as brain computer interfaces. Support vector machines (SVM) have been particularly popular for MVPA owing to their high prediction accuracy even with noisy datasets. Recent work has proposed the use of relevance vector machines (RVM) as an alternative to SVM. RVMs are particularly attractive in time sensitive applica

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Albardan, Mahmoud. "Combinaison robuste à la dépendance entre classifieurs dans un contexte d’apprentissage décentralisé." Thesis, Lille 1, 2018. http://www.theses.fr/2018LIL1I050/document.

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L’apprentissage automatique est un domaine en forte croissance à la fois dans le nombre de méthodes employées mais aussi dans le nombre de base de données mises à disposition des utilisateurs. La classification, qui est une tâche qui peut être abordée par l’apprentissage artificiel, est ainsi affectée par ce changement. La présence d’un grand nombre d’algorithmes de classification incite alors à créer des systèmes globaux −basés sur des comités de classifieurs− dont le but est de donner des solutions plus efficaces pour les problèmes de classification complexes. Pour cette raison, on traite da

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Quost, Benjamin. "Combinaison de classifieurs binaires dans le cadre des fonctions de croyance." Compiègne, 2006. http://www.theses.fr/2006COMP1647.

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La classification supervisée a pour enjeu de construire un système, ou classifieur, capable de prédire automatiquement la classe d'un phénomène observé. Son architecture peut être modulaire : le problème abordé est décomposé en sous-problèmes plus simples, traités par des classifieurs, et la combinaison des résultats donne la solution globale. Nous nous intéressons au cas de sous-problèmes binaires, en particulier les décompositions où chaque classe est opposée à chaque autre, chaque classe est opposée à toutes les autres, et le cas général où deux groupes de classes disjoints sont opposés l'u

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Abou, Latif Firas. "Identification du profil des utilisateurs d'un hypermédia encyclopédique à l'aide de classifieurs basés sur des dissimilarités : création d'un composant d'un système expert pour Hypergéo." Phd thesis, INSA de Rouen, 2011. http://tel.archives-ouvertes.fr/tel-00625439.

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L'objectif de cette thèse est d'identifier le profil d'utilisateur d'un hypermédia afin de l'adapter. Ceprofil est déterminé en utilisant des algorithmes d'apprentissage supervisé comme le SVM.Le modèle d'utilisateur est l'un des composants essentiels des hypermédias adaptatifs. Une des façons de caractériser ce modèle est d'associer l'utilisateur à un profil. Le Web Usage Mining (WUM)identifie ce profil à l'aide des traces de navigation. Toutefois, ces techniques ne fonctionnent généralement que sur de gros volumes de données. Dans le cadre de volumes de données réduits, nous proposons d'util

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Santos, Araken de Medeiros. "Investigando a combina??o de t?cnicas de aprendizado semissupervisionado e classifica??o hier?rquica multirr?tulo." Universidade Federal do Rio Grande do Norte, 2012. http://repositorio.ufrn.br:8080/jspui/handle/123456789/18690.

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Made available in DSpace on 2015-03-03T15:48:39Z (GMT). No. of bitstreams: 1 ArakenMS_TESE.pdf: 4060697 bytes, checksum: 5efe25ac134a602cc32c96b66e749ea0 (MD5) Previous issue date: 2012-05-25<br>Data classification is a task with high applicability in a lot of areas. Most methods for treating classification problems found in the literature dealing with single-label or traditional problems. In recent years has been identified a series of classification tasks in which the samples can be labeled at more than one class simultaneously (multi-label classification). Additionally, these classes can

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Gacquer, David. "Sur l'utilisation active de la diversité dans la construction d'ensembles de classifieurs. Application à la détection de fumées nocives sur site industriel." Phd thesis, Université de Valenciennes et du Hainaut-Cambresis, 2008. http://tel.archives-ouvertes.fr/tel-00392616.

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L'influence de la diversité lors de la construction d'ensembles de classifieurs a soulevé de nombreuses discussions au sein de la communauté de l'Apprentissage Automatique ces dernières années. <br> Une manière particulière de construire un ensemble de classifieurs consiste à sélectionner individuellement les membres de l'ensemble à partir d'un pool de classifieurs en se basant sur des critères prédéfinis. <br> La littérature fait référence à cette méthode sous le terme de paradigme Surproduction et Sélection, également appelé élagage d'ensemble de classifieurs.<br> <br> Les travaux présentés

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Guigou, Fabio. "The artificial immune ecosystem : a scalable immune-inspired active classifier, an application to streaming time series analysis for network monitoring." Thesis, Strasbourg, 2019. http://www.theses.fr/2019STRAD007/document.

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Introduits au début des années 1990, les systèmes immunitaires artificiels visent à adapter les propriétés du système immunitaire biologique, telles que sa scalabilité et son adaptivité, à des problèmes informatiques : sécurité, mais également optimisation et classification. Cette thèse explore une nouvelle direction en se concentrant non sur les processus biologiques et les cellules elles-mêmes, mais sur les interactions entre les sous-systèmes. Ces modes d’interaction engendrent les propriétés reconnues du système immunitaire : détection d’anomalies, reconnaissance des pathogènes connus, réa

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Artchounin, Daniel. "Tuning of machine learning algorithms for automatic bug assignment." Thesis, Linköpings universitet, Programvara och system, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139230.

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In software development projects, bug triage consists mainly of assigning bug reports to software developers or teams (depending on the project). The partial or total automation of this task would have a positive economic impact on many software projects. This thesis introduces a systematic four-step method to find some of the best configurations of several machine learning algorithms intending to solve the automatic bug assignment problem. These four steps are respectively used to select a combination of pre-processing techniques, a bug report representation, a potential feature selection tec

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Dhouib, Sofiane. "Contributions to unsupervised domain adaptation : Similarity functions, optimal transport and theoretical guarantees." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEI117.

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L'explosion de la quantité de données produites chaque jour a fait de l' l'Apprentissage Automatique un outil vital pour extraire des motifs de haute valeur à partir de celles-là. Concrètement, un algorithme d'apprentissage automatique apprend de tels motifs après avoir été entraîné sur un jeu de données appelé données d'entraînement, et sa performance est évaluée sur échantillon différent, appelé données de test. L'Adaptation de Domaine est une branche de l'apprentissage automatique, dans lequel les données d'entraînement et de test ne sont plus supposées provenir de la même distribution de p

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Silva, Victor Anselmo. "Redes lógicas de Markov aplicadas ao aprendizado de classificadores automáticos de dados." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/3/3152/tde-18052011-151242/.

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Sistemas de computação têm se tornado maiores e mais complexos com o objetivo de lidar com a vasta quantidade de dados disponíveis. Uma tarefa decisiva em tais sistemas é classificar estes dados, bem como extrair informação útil destes. Nesta dissertação, testam-se as redes lógicas de Markov como linguagem para especificação e aprendizado de classificadores automáticos de dados. Esta linguagem combina fragmentos da lógica de primeira ordem e modelos probabilísticos gráficos (redes de Markov) em uma única representação. A junção destas duas técnicas permite a modelagem de conhecimento relaciona

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Heidfors, Filip, and Elias Moltedo. "Maskininlärning: avvikelseklassificering på sekventiell sensordata. En jämförelse och utvärdering av algoritmer för att klassificera avvikelser i en miljövänlig IoT produkt med sekventiell sensordata." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20742.

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Ett företag har tagit fram en miljövänlig IoT produkt med sekventiell sensordata och vill genom maskininlärning kunna klassificera avvikelser i sensordatan. Det har genom åren utvecklats ett flertal väl fungerande algoritmer för klassificering men det finns emellertid ingen algoritm som fungerar bäst för alla olika problem. Syftet med det här arbetet var därför att undersöka, jämföra och utvärdera olika klassificerare inom "supervised machine learning" för att ta reda på vilken klassificerare som ger högst träffsäkerhet att klassificera avvikelser i den typ av IoT produkt som företaget tagit f

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Coletta, Luiz Fernando Sommaggio. "Abordagens para combinar classificadores e agrupadores em problemas de classificação." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-24032016-102229/.

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Modelos para aprendizado não supervisionado podem fornecer restrições complementares úteis para melhorar a capacidade de generalização de classificadores. Baseando-se nessa premissa, um algoritmo existente, denominado de C3E (Consensus between Classification and Clustering Ensembles), recebe como entradas estimativas de distribuições de probabilidades de classes para objetos de um conjunto alvo, bem como uma matriz de similaridades entre esses objetos. Tal matriz é tipicamente construída por agregadores de agrupadores de dados, enquanto que as distribuições de probabilidades de classes são obt

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Silva, Gabriella Cynara Minora da. "Diagn?stico da degrada??o ambiental no munic?pio de Areia Branca-RN por geotecnologias." Universidade Federal do Rio Grande do Norte, 2013. http://repositorio.ufrn.br:8080/jspui/handle/123456789/18247.

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Made available in DSpace on 2014-12-17T15:55:04Z (GMT). No. of bitstreams: 1 GabriellaCMS_DISSERT.pdf: 3451606 bytes, checksum: 1ee33015b3633fb79771b6160bff6a6c (MD5) Previous issue date: 2013-02-25<br>Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior<br>The municipality of Areia Branca is within the mesoregion of West Potiguar and within the microregion of Mossor?, covering an area of 357,58 km2. Covering an area of weakness in terms of environmental, housing, together with the municipality of Grossos-RN, the estuary of River Apodi-Mossor?. The municipality of Areia Branca has his

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Becq, Guillaume. "Contribution au développement d'un système portable automatique d'aide à la détermination de l'état d'urgence d'un blessé polytraumatisé." Phd thesis, Université Joseph Fourier (Grenoble), 2004. http://www.theses.fr/2004GRE10199.

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Dans cette thèse, nous proposons et évaluons la mise en place de la chaîne complète de traitement de "information pour la déterminatiol de l'état de santé d'un blessé polytraumatisé. Nous faisons l'hypothèse que l'état de santé du blessé est observable dans un espace généré à partir de caractéristiques extraites sur des mesures provenant de capteurs portables. Au cours du polytraumatisme, son état se déplace dans des régions propres à chaque classe de choc hémorragique rencontrée, indépendante de l'individu. Le but de cette étude est de trouver ces régions par apprentissage supervisé pour un e

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Rocha, Jocielma Jerusa Leal. "APLICAÇÃO DE TÉCNICAS DE APRENDIZADO DE MÁQUINA PARA CLASSIFICAÇÃO DE DEPÓSITOS MINERAIS BASEADA EM MODELO TEOR-TONELAGEM." Universidade Federal do Maranhão, 2010. http://tedebc.ufma.br:8080/jspui/handle/tede/444.

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Made available in DSpace on 2016-08-17T14:53:11Z (GMT). No. of bitstreams: 1 Jocielma Jerusa Leal Rocha.pdf: 3008647 bytes, checksum: 785c07837e5e5bb39cb7685000c9d145 (MD5) Previous issue date: 2010-07-01<br>Classification of mineral deposits into types is traditionally done by experts. Since there are reasons to believe that computational techniques can aid this classification process and make it less subjective, the research and investigation of different methods of clustering and classification to this domain may be appropriate. The way followed by researches in this domain has directed

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Ragot, Nicolas. "MÉLIDIS : Reconnaissance de formes par modélisation mixte intrinsèque/discriminante à base de systèmes d'inférence floue hiérarchisés." Phd thesis, Rennes 1, 2003. http://www.theses.fr/2003REN10078.

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Pour faciliter la mise au point de systèmes de reconnaissance de formes, nous proposons une méthodologie de classification visant à réunir un ensemble de propriétés rarement satisfaites dans une même approche : performances, généricité, fiabilité, robustesse, compacité et interprétabilité. Ce dernier point permet au concepteur d'adapter, de maintenir et d'optimiser le système plus facilement. L’approche proposée, centrée sur la notion de connaissances dans un classifieur, est entièrement guidée par les données. L’originalité réside notamment dans l’exploitation conjointe de c

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Konečný, Antonín. "Využití umělé inteligence v technické diagnostice." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-443221.

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The diploma thesis is focused on the use of artificial intelligence methods for evaluating the fault condition of machinery. The evaluated data are from a vibrodiagnostic model for simulation of static and dynamic unbalances. The machine learning methods are applied, specifically supervised learning. The thesis describes the Spyder software environment, its alternatives, and the Python programming language, in which the scripts are written. It contains an overview with a description of the libraries (Scikit-learn, SciPy, Pandas ...) and methods — K-Nearest Neighbors (KNN), Support Vector Machi

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Lai, Chia-Shing, and 賴家馨. "Supervised Fuzzy ART: a Fast Neural Network Classifier Trained via Combining Supervised and Unsupervised Learning." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/11797317646024007492.

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碩士<br>國立臺灣科技大學<br>工程技術研究所<br>81<br>A neural network model, called supervised fuzzy ART (SFART), that incorporates a supervised mechanism into a modified fuzzy ART is presented. In any time, the training instances may or may not have desired outputs, that is, this model can handle supervised and unsupervised learning simultaneously. The unsupervised component finds the cluster relations of instances, then the supervised component learns the desired associations between clusters and classes

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Acharya, Ayan. "Combining classifier and cluster ensembles for semi-supervised and transfer learning." Thesis, 2012. http://hdl.handle.net/2152/ETD-UT-2012-05-5086.

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Unsupervised models can provide supplementary soft constraints to help classify new, "target" data since similar instances in the target set are more likely to share the same class label. Such models can also help detect possible differences between training and target distributions, which is useful in applications where concept drift may take place, as in transfer learning settings. This contribution describes two general frameworks that take as input class membership estimates from existing classifiers learnt on previously encountered "source" data, as well as a set of cluster labels from a

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Srinivasu, Ulli. "Semi-Supervised Learning in Random Forest Classifier for Human Action Recognition." Thesis, 2018. http://ethesis.nitrkl.ac.in/9967/1/2018_MT_216EC6242_USrinivasu_Semi.pdf.

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The objective of human action recognition is the interpretation of ongoing events and context from a video data for automated systems. Because of intra class variations and complex background, human action recognition is a very challenging task. In this work, a feature extraction technique viz histogram of 3D oriented gradients (HOG3D) and histogram of3D optical flow (HOF3D) are used to describe an action prominently. Video sequence is characterized by spatio-temporal interest points (STIPs) to represent the video sparsely and effectively for less complexity. STIPs describe the significant var

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Jung, Jae. "Automatic Assignment of Protein Function with Supervised Classifiers." 2008. http://hdl.handle.net/1969.1/ETD-TAMU-2008-08-41.

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High-throughput genome sequencing and sequence analysis technologies have created the need for automated annotation and analysis of large sets of genes. The Gene Ontology (GO) provides a common controlled vocabulary for describing gene function. However, the process for annotating proteins with GO terms is usually through a tedious manual curation process by trained professional annotators. With the wealth of genomic data that are now available, there is a need for accurate auto- mated annotation methods. The overall objective of my research is to improve our ability to automatically an- notat

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