Relevant bibliographies by topics / Bayesian classification / Dissertations / Theses
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Dissertations / Theses on the topic 'Bayesian classification'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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1

Nappa, Dario. "Bayesian classification using Bayesian additive and regression trees." Ann Arbor, Mich. : ProQuest, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3336814.

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Thesis (Ph.D. in Statistical Sciences)--S.M.U.<br>Title from PDF title page (viewed Mar. 16, 2009). Source: Dissertation Abstracts International, Volume: 69-12, Section: B, page: . Adviser: Xinlei Wang. Includes bibliographical references.
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Haywood, Andries Stefan. "Bayesian object classification in nanoimages." Diss., University of Pretoria, 2017. http://hdl.handle.net/2263/63790.

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In this mini-dissertation the importance of having an automated object classification procedure for classifying nanoparticles in nanoscale images (or referred to as nanoimages in this mini-dissertation) is discussed, and a detailed overview of such a procedure, proposed by Konomi et al. (2013) is provided, with emphasis on applying the procedure to nanoimages of gold nanoparticles. In the process a simplified approach to classifying occluded objects when dealing with homogeneously shaped objects is introduced. Nanotechnology is a technology that deals with measurements obtained in nano-scale (
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Anderson, Michael P. "Bayesian classification of DNA barcodes." Diss., Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/2247.

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4

Gibbs, M. N. "Bayesian Gaussian processes for regression and classification." Thesis, University of Cambridge, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.599379.

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Bayesian inference offers us a powerful tool with which to tackle the problem of data modelling. However, the performance of Bayesian methods is crucially dependent on being able to find good models for our data. The principal focus of this thesis is the development of models based on Gaussian process priors. Such models, which can be thought of as the infinite extension of several existing finite models, have the flexibility to model complex phenomena while being mathematically simple. In this thesis, I present a review of the theory of Gaussian processes and their covariance functions and de
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De, Lance Holmes Christopher Charles. "Bayesian method for nonlinear classification and regression." Thesis, Imperial College London, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.394926.

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Chan, Kwokleung. "Bayesian learning in classification and density estimation /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC IP addresses, 2002. http://wwwlib.umi.com/cr/ucsd/fullcit?p3061619.

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7

Morales, quinga Katherine Tania. "Generative Markov models for sequential bayesian classification." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAS019.

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Cette thèse vise à modéliser des données séquentielles à travers l'utilisation de modèles probabilistes à variables latentes et paramétrés par des architectures de type réseaux de neurones profonds. Notre objectif est de développer des modèles dynamiques capables de capturer des dynamiques temporelles complexes inhérentes aux données séquentielles tout en étant applicables dans des domaines variés tels que la classification, la prédiction et la génération de données pour n'importe quel type de données séquentielles. Notre approche se concentre sur plusieurs problématiques liés à la modélisatio
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Wang, Xiaohui. "Bayesian classification and survival analysis with curve predictors." [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1205.

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Loza, Reyes Elisa. "Classification of phylogenetic data via Bayesian mixture modelling." Thesis, University of Bath, 2010. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.519916.

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Conventional probabilistic models for phylogenetic inference assume that an evolutionary tree,andasinglesetofbranchlengthsandstochasticprocessofDNA evolutionare sufficient to characterise the generating process across an entire DNA alignment. Unfortunately such a simplistic, homogeneous formulation may be a poor description of reality when the data arise from heterogeneous processes. A well-known example is when sites evolve at heterogeneous rates. This thesis is a contribution to the modelling and understanding of heterogeneityin phylogenetic data. Weproposea methodfor the classificationof DN
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Cooley, Craig Allen. "Bayesian and nonparametric models in the classification problem /." The Ohio State University, 1996. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487935573773741.

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Schmidt, Aurora Clare 1981. "Dynamic Bayesian networks for the classification of spinning discs." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/16686.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.<br>Includes bibliographical references (p. 87-89).<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>This thesis considers issues for the application of particle filters to a class of nonlinear filtering and classification problems. Specifically, we study a prototype system of spinning discs. The system combines linear dynamics describing rotation with a nonlinear observation m
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Greenewald, Kristjan H. "Prediction of Optimal Bayesian Classification Performance for LADAR ATR." Wright State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=wright1347302998.

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Tyni, Elin, and Johanna Wikberg. "Classification of Wi-Fi Sensor Data for a Smarter City : Probabilistic Classification using Bayesian Statistics." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-159797.

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As cities are growing with an increasing number of residents, problems with the traffic such as congestion and larger emission arise. The city planners have challenges with making it as easy as possible for the residents to commute and in as large scale as possible to avoid vehicles. Before any improvements or reconstructions can be made, the traffic situation has to be mapped. The results from a probabilistic classification on Wi-Fi sensor data collected in an area in the southern part of Stockholm showed that some streets are more likely to be trafficked by cyclists than pedestrians while ot
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Mancill, Paul Anthony. "An exploration of naïve Bayesian classification augmented with confidence intervals." Pullman, Wash. : Washington State University, 2010. http://www.dissertations.wsu.edu/Thesis/Summer2010/p_mancill_041310.pdf.

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Thesis (M.S. in computer science)--Washington State University, May 2010.<br>Title from PDF title page (viewed on May 14, 2010). "School of Engineering and Computer Science." Includes bibliographical references (p. 85-90).
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Frey, Brendan J. "Bayesian networks for pattern classification, data compression, and channel coding." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape16/PQDD_0017/NQ27647.pdf.

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Gehrke, Grant T. "Authorship discovery in blogs using Bayesian classification with corrective scaling." Thesis, Monterey, Calif. : Naval Postgraduate School, 2008. http://handle.dtic.mil/100.2/ADA483774.

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Thesis (M.S. in Computer Science)--Naval Postgraduate School, June 2008.<br>Thesis Advisor(s): Martell, Craig H. "June 2008." Description based on title screen as viewed on August 22, 2008. Includes bibliographical references (p. 33-36). Also available in print.
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Ceccon, Stefano. "Extending Bayesian network models for mining and classification of glaucoma." Thesis, Brunel University, 2013. http://bura.brunel.ac.uk/handle/2438/8051.

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Glaucoma is a degenerative disease that damages the nerve fiber layer in the retina of the eye. Its mechanisms are not fully known and there is no fully-effective strategy to prevent visual impairment and blindness. However, if treatment is carried out at an early stage, it is possible to slow glaucomatous progression and improve the quality of life of sufferers. Despite the great amount of heterogeneous data that has become available for monitoring glaucoma, the performance of tests for early diagnosis are still insufficient, due to the complexity of disease progression and the diffculties in
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Ruz, Heredia Gonzalo Andres. "Bayesian networks for classification, clustering, and high-dimensional data visualisation." Thesis, Cardiff University, 2008. http://orca.cf.ac.uk/54722/.

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This thesis presents new developments for a particular class of Bayesian networks which are limited in the number of parent nodes that each node in the network can have. This restriction yields structures which have low complexity (number of edges), thus enabling the formulation of optimal learning algorithms for Bayesian networks from data. The new developments are focused on three topics: classification, clustering, and high-dimensional data visualisation (topographic map formation). For classification purposes, a new learning algorithm for Bayesian networks is introduced which generates sim
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Kasai, Eli Kunwiji. "SALT spectroscopy and classification of supernova spectra using Bayesian techniques." Doctoral thesis, University of Cape Town, 2017. http://hdl.handle.net/11427/27283.

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In this thesis, we present the Southern African Large Telescope spectroscopic follow-up programme for supernova candidates discovered by the international Dark Energy Survey, the goals of which are to measure the expansion history of the Universe and shed light on the mysterious nature of dark energy. In total, we took spectra for 36 supernova candidates. These were classified using a new Bayesian Supernova spectra classifier, SuperNovaMC, that we developed to address limitations with existing algorithms. SuperNovaMC simultaneously finds the best fitting supernova and host galaxy using Bayesia
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Kalkandara, Karolina. "Neural networks and classification trees for misclassified data." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.312187.

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Harmouche, Rola. "Bayesian multiple sclerosis lesion classification modeling regional and local spatial information." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=99411.

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This thesis presents a fully automatic Bayesian method for multiple sclerosis lesion classification. Traditionally, human experts locate lesions, which are diseased tissue, on magnetic resonance images (MRI). However, manual classification methods are particularly subjective, as experts locate lesions differently, particularly around the borders of these structures. The proposed approach classifies voxels from MRIs into regular tissue and lesions, thus allowing for an objective and consistent way to locate lesions in order to help track their size and count. Previous automatic classification a
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Ehtiati, Tina. "Strongly coupled Bayesian models for interacting object and scene classification processes." Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=102975.

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In this thesis, we present a strongly coupled data fusion architecture within a Bayesian framework for modeling the bi-directional influences between the scene and object classification mechanisms. A number of psychophysical studies provide experimental evidence that the object and the scene perception mechanisms are not functionally separate in the human visual system. Object recognition facilitates the recognition of the scene background and also knowledge of the scene context facilitates the recognition of the individual objects in the scene. The evidence indicating a bi-directional exchang
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Davis, Justin Kyle. "Bayesian model selection for classification with possibly large number of groups." Doctoral diss., University of Central Florida, 2011. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4757.

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The purpose of the present dissertation is to study model selection techniques which are specifically designed for classification of high-dimensional data with a large number of classes. To the best of our knowledge, this problem has never been studied in depth previously. We assume that the number of components p is much larger than the number of samples n, and that only few of those p components are useful for subsequent classification. In what follows, we introduce two Bayesian models which use two different approaches to the problem: one which discards components which have "almost constan
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Acosta, Mena Dionisio M. "Statistical classification of magnetic resonance imaging data." Thesis, University of Sussex, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390913.

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Orre, Roland. "On Data Mining and Classification Using a Bayesian Confidence Propagation Neural Network." Doctoral thesis, KTH, Numerical Analysis and Computer Science, NADA, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3592.

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<p>The aim of this thesis is to describe how a statisticallybased neural network technology, here named BCPNN (BayesianConfidence Propagation Neural Network), which may be identifiedby rewriting Bayes' rule, can be used within a fewapplications, data mining and classification with credibilityintervals as well as unsupervised pattern recognition.</p><p>BCPNN is a neural network model somewhat reminding aboutBayesian decision trees which are often used within artificialintelligence systems. It has previously been success- fullyapplied to classification tasks such as fault diagnosis,supervised pa
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McCormick, Neil Howie. "Bayesian methods for automatic segmentation and classification of SLO and SONAR data." Thesis, Heriot-Watt University, 2001. http://hdl.handle.net/10399/452.

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Stampoulis, Vasileios. "Bayesian estimation of luminosity distributions and model based classification of astrophysical sources." Thesis, Imperial College London, 2017. http://hdl.handle.net/10044/1/59358.

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The distribution of the flux (observed luminosity) of astrophysical objects is of great interest as a measure of the evolution of various types of astronomical source populations and for testing theoretical assumptions about the Universe. This distribution is examined using the cumulative distribution of the number of sources (N) detected at a given flux (S), known as the log(N)−log(S) curve to astronomers. Estimating the log(N) − log(S) curve from observational data can be quite challenging though, since statistical fluctuations in the measurements and detector biases often lead to measuremen
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Hudson, Richard Earl. "Semi-Supervised Visual Texture Based Pattern Classification." Case Western Reserve University School of Graduate Studies / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=case1339081444.

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Kim, Jong Hwan. "Autonomous Navigation, Perception and Probabilistic Fire Location for an Intelligent Firefighting Robot." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/64997.

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Firefighting robots are actively being researched to reduce firefighter injuries and deaths as well as increase their effectiveness on performing tasks. There has been difficulty in developing firefighting robots that autonomously locate a fire inside of a structure that is not in the direct robot field of view. The commonly used sensors for robots cannot properly function in fire smoke-filled environments where high temperature and zero visibility are present. Also, the existing obstacle avoidance methods have limitations calculating safe trajectories and solving local minimum problem while a
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CODECASA, DANIELE. "Continuous time bayesian network classifiers." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2014. http://hdl.handle.net/10281/80691.

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Streaming data are relevant to finance, computer science, and engineering, while they are becoming increasingly important to medicine and biology. Continuous time Bayesian networks are designed for analyzing efficiently multivariate streaming data, exploiting the conditional independencies in continuous time homogeneous Markov processes. Continuous time Bayesian network classifiers are a specialization of continuous time Bayesian networks designed for multivariate streaming data classification when time duration of events matters and the class occurs in the future. Continuous time Bayesian net
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Rios, Felix Leopoldo. "Bayesian inference in probabilistic graphical models." Doctoral thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214542.

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This thesis consists of four papers studying structure learning and Bayesian inference in probabilistic graphical models for both undirected and directed acyclic graphs (DAGs). Paper A presents a novel algorithm, called the Christmas tree algorithm (CTA), that incrementally construct junction trees for decomposable graphs by adding one node at a time to the underlying graph. We prove that CTA with positive probability is able to generate all junction trees of any given number of underlying nodes. Importantly for practical applications, we show that the transition probability of the CTA kernel
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Petersson, Andreas. "Data mining file sharing metadata : A comparison between Random Forests Classification and Bayesian Networks." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-11285.

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In this comparative study based on experimentation it is demonstrated that the two evaluated machine learning techniques, Bayesian networks and random forests, have similar predictive power in the domain of classifying torrents on BitTorrent file sharing networks.This work was performed in two steps. First, a literature analysis was performed to gain insight into how the two techniques work and what types of attacks exist against BitTorrent file sharing networks. After the literature analysis, an experiment was performed to evaluate the accuracy of the two techniques.The results show no signif
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Tsiftsi, Thomai. "Statistical shape analysis in a Bayesian framework : the geometric classification of fluvial sand bodies." Thesis, Durham University, 2015. http://etheses.dur.ac.uk/11368/.

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We present a novel shape classification method which is embedded in the Bayesian paradigm. We focus on the statistical classification of planar shapes by using methods which replace some previous approximate results by analytic calculations in a closed form. This gives rise to a new Bayesian shape classification algorithm and we evaluate its efficiency and efficacy on available shape databases. In addition we apply our results to the statistical classification of geological sand bodies. We suggest that our proposed classification method, that utilises the unique geometrical information of the
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Darcy, Peter. "Resolving RFID Anomalies using Intelligent Analysis and Classification." Thesis, Griffith University, 2012. http://hdl.handle.net/10072/366922.

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Radio Frequency Identication (RFID) technology refers to the use of tags with unique identiers being attached to various items which are scanned without a line of sight and then recorded into a database. Current integrations of this technology include baggage tracking at airports, pet owner identication and tagging objects in stores to enforce security by alerting management when an item has left the facility without the tag being deactivated. Despite the wide-scale adoption and advantages of RFID, several issues exist that introduce a level of unreliability resulting in the technology only be
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Pflugeisen, Bethann Mangel. "Analysis of Otolith Microchemistry Using Bayesian Hierarchical Mixture Models." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1275059376.

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36

Fredlund, Richard. "A Bayesian expected error reduction approach to Active Learning." Thesis, University of Exeter, 2011. http://hdl.handle.net/10036/3170.

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There has been growing recent interest in the field of active learning for binary classification. This thesis develops a Bayesian approach to active learning which aims to minimise the objective function on which the learner is evaluated, namely the expected misclassification cost. We call this approach the expected cost reduction approach to active learning. In this form of active learning queries are selected by performing a `lookahead' to evaluate the associated expected misclassification cost. \paragraph{} Firstly, we introduce the concept of a \textit{query density} to explicitly model ho
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Amlinger, Anton. "An Evaluation of Clustering and Classification Algorithms in Life-Logging Devices." Thesis, Linköpings universitet, Programvara och system, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-121630.

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Using life-logging devices and wearables is a growing trend in today’s society. These yield vast amounts of information, data that is not directly overseeable or graspable at a glance due to its size. Gathering a qualitative, comprehensible overview over this quantitative information is essential for life-logging services to serve its purpose. This thesis provides an overview comparison of CLARANS, DBSCAN and SLINK, representing different branches of clustering algorithm types, as tools for activity detection in geo-spatial data sets. These activities are then classified using a simple model w
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Steckenrider, John J. "Multi-Bayesian Approach to Stochastic Feature Recognition in the Context of Road Crack Detection and Classification." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/81752.

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This thesis introduces a multi-Bayesian framework for detection and classification of features in environments abundant with error-inducing noise. The approach takes advantage of Bayesian correction and classification in three distinct stages. The corrective scheme described here extracts useful but highly stochastic features from a data source, whether vision-based or otherwise, to aid in higher-level classification. Unlike many conventional methods, these features’ uncertainties are characterized so that test data can be correctively cast into the feature space with probability distribution
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Severini, Jérôme. "Estimation et Classification de Signaux Altimétriques." Thesis, Toulouse, INPT, 2010. http://www.theses.fr/2010INPT0125/document.

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La mesure de la hauteur des océans, des vents de surface (fortement liés aux températures des océans), ou encore de la hauteur des vagues sont un ensemble de paramètres nécessaires à l'étude des océans mais aussi au suivi de leurs évolutions : l'altimétrie spatiale est l'une des disciplines le permettant. Une forme d'onde altimétrique est le résultat de l'émission d'une onde radar haute fréquence sur une surface donnée (classiquement océanique) et de la mesure de la réflexion de cette onde. Il existe actuellement une méthode d'estimation non optimale des formes d'onde altimétriques ainsi que d
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Hsieh, M. C. M. "Alternative Bayesian techniques for model selection, classification, and parameter estimation in signal and image processing." Thesis, University of Cambridge, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.604678.

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This thesis intends to address some key aspects of the implementation of Bayesian analysis for classification, model selection and parameter estimation, and proposes three enhancements or alternative approaches to commonly employed techniques. 1. An extension of the General Linear Model allows prior parameter information to be included whilst retaining the analytic and accessible form of the model evidence and posterior distribution. Channel estimation in non-stationary noise and retrospective excitation changepoint detection are used as illustrations of the extended model's applicability. 2.
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Zhang, Jufen. "Bayesian density estimation and classification of incomplete data using semi-parametric and non parametric models." Thesis, University of Exeter, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.426082.

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Gutiérrez, Ayala Evelyn Patricia. "Estimation of the disease prevalence when diagnostic tests are subject to classification error: bayesian approach." Master's thesis, Pontificia Universidad Católica del Perú, 2016. http://tesis.pucp.edu.pe/repositorio/handle/123456789/7631.

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La estimación de la prevalencia de una enfermedad, la cual es definida como el número de casos con la enfermedad en una población dividida por el número de elementos en ésta, es realizado con gran precisión cuando existen pruebas 100% exactas, también llamadas gold standard. Sin embargo, en muchos casos, debido a los altos costos de las pruebas de diagnóstico o limitaciones de tecnología, la prueba gold standard no existe y debe ser reemplazada por una o más pruebas diagnósticas no tan caras pero con bajos niveles de sensibilidad o especificidad. Este estudio está enfocado en el estudio de dos
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Rebholz, Matthew John. "Dynamic Spectrum Access Network Simulation and Classification of Secondary User Properties." Thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/23244.

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This thesis explores the use of the Naïve Bayesian classifier as a method of determining high-level information about secondary users in a Dynamic Spectrum Access (DSA) network using a low complexity channel sensing method.  With a growing number of users generating an increased demand for broadband access, determining an efficient method for utilizing the limited available broadband is a developing current and future issue.  One possible solution is DSA, which we simulate using the Universal DSA Network Simulator (UDNS), created by our team at Virginia Tech.<br />However, DSA requires user de
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Berrett, Candace. "Bayesian Probit Regression Models for Spatially-Dependent Categorical Data." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1285076512.

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Zens, Gregor. "Bayesian shrinkage in mixture-of-experts models: identifying robust determinants of class membership." Springer, 2019. http://dx.doi.org/10.1007/s11634-019-00353-y.

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A method for implicit variable selection in mixture-of-experts frameworks is proposed. We introduce a prior structure where information is taken from a set of independent covariates. Robust class membership predictors are identified using a normal gamma prior. The resulting model setup is used in a finite mixture of Bernoulli distributions to find homogenous clusters of women in Mozambique based on their information sources on HIV. Fully Bayesian inference is carried out via the implementation of a Gibbs sampler.
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Napier, Gary. "A Bayesian hierarchical model of compositional data with zeros : classification and evidence evaluation of forensic glass." Thesis, University of Glasgow, 2014. http://theses.gla.ac.uk/5793/.

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A Bayesian hierarchical model is proposed for modelling compositional data containing large concentrations of zeros. Two data transformations were used and compared: the commonly used additive log-ratio (alr) transformation for compositional data, and the square root of the compositional ratios. For this data the square root transformation was found to stabilise variability in the data better. The square root transformation also had no issues dealing with the large concentrations of zeros. To deal with the zeros, two different approaches have been implemented: the data augmentation approach an
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Clark, Andrew Robert James. "Multi-objective ROC learning for classification." Thesis, University of Exeter, 2011. http://hdl.handle.net/10036/3530.

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Receiver operating characteristic (ROC) curves are widely used for evaluating classifier performance, having been applied to e.g. signal detection, medical diagnostics and safety critical systems. They allow examination of the trade-offs between true and false positive rates as misclassification costs are varied. Examination of the resulting graphs and calcu- lation of the area under the ROC curve (AUC) allows assessment of how well a classifier is able to separate two classes and allows selection of an operating point with full knowledge of the available trade-offs. In this thesis a multi-obj
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Ali, Khan Syed Irteza. "Classification using residual vector quantization." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50300.

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Residual vector quantization (RVQ) is a 1-nearest neighbor (1-NN) type of technique. RVQ is a multi-stage implementation of regular vector quantization. An input is successively quantized to the nearest codevector in each stage codebook. In classification, nearest neighbor techniques are very attractive since these techniques very accurately model the ideal Bayes class boundaries. However, nearest neighbor classification techniques require a large size of representative dataset. Since in such techniques a test input is assigned a class membership after an exhaustive search the entire training
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Tang, Adelina Lai Toh. "Application of the tree augmented naive Bayes network to classification and forecasting /." [St. Lucia, Qld.], 2004. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe.pdf.

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Renaud, Gabriel. "Bayesian maximum a posteriori algorithms for modern and ancient DNA." Doctoral thesis, Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-195705.

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When DNA is sequenced, nucleotide calls are produced along with their individual error probabilities, which are usually reported in the form of a per-base quality score. However, these quality scores have not generally been incorporated into probabilistic models as there is typically a poor correlation between the predicted and observed error rates. Computational tools aimed at sequence analysis have therefore used arbitrary cutoffs on quality scores which often unnecessarily reduce the amount of data that can be analyzed. A different approach involves recalibration of those quality scores usi
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