Tesis sobre el tema "Bayesian classification"
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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.
Texto completoTitle from PDF title page (viewed Mar. 16, 2009). Source: Dissertation Abstracts International, Volume: 69-12, Section: B, page: . Adviser: Xinlei Wang. Includes bibliographical references.
Haywood, Andries Stefan. "Bayesian object classification in nanoimages". Diss., University of Pretoria, 2017. http://hdl.handle.net/2263/63790.
Texto completoMini Dissertaion (MSc)--University of Pretoria, 2017.
NRF (under CSUR grant 90315)
CSIR
Statistics
MSc
Unrestricted
Anderson, Michael P. "Bayesian classification of DNA barcodes". Diss., Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/2247.
Texto completoGibbs, 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.
Texto completoDe, 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.
Texto completoChan, 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.
Texto completoWang, 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.
Texto completoLoza, 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.
Texto completoCooley, Craig Allen. "Bayesian and nonparametric models in the classification problem /". The Ohio State University, 1996. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487935573773741.
Texto completoSchmidt, 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.
Texto completoIncludes bibliographical references (p. 87-89).
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
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 model determined by the disc pattern, which is parameterized by angle. A consequence of the nonlinear observation model is that the posterior state distribution of angle and spin-rate is multi-modal. This detail motivates the use of particle filtering. Practical issues that we consider when using particle filters are sample depletion and sample degeneracy, both of which lead to poor representations of the state distributions. Variance based resampling and regularization are common methods to mitigate sampling issues in particle filtering. We investigate these methods empirically for our prototype problem. Specific parameters of interest relating to these methods are the number of particles used to approximate the posterior distribution, quantitative methods for deciding when to resample, choice of regularization variance, the impact of measurement noise on all of these, and performance over time. A common issue, leading to inaccurate sample-based representations, is the case of relatively low measurement noise combined with an insufficient number of particles. Our empirical results show that for relatively smooth patterns (e.g. linear, cosine) particle filters were less susceptible to sampling issues than for patterns with higher frequency content. The goal of our experiments is to quantify the nature of these differences.
by Aurora Clare Schmidt.
M.Eng.
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.
Texto completoTyni, Elin y 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.
Texto completoI takt med att städer växer med ökat antal invånare uppståar det problem i trafiken såsom trängsel och utsläpp av partiklar. Trafikplanerare ställs inför utmaningar i form av hur de kan underlätta pendling för invånarna och hur de, i så stor utsträckning som möjligt, kan minska fordon i tätorten. Innan potentiella förbättringar och ombyggnationer kan genomföras måste trafiken kartläggas. Resultatet från en sannolikhetsklassificering på Wi-Fi sensordata insamlat i ett område i södra delen av Stockholm visar att vissa gator är mer trafikerade av cyclister än fotgängare medan andra gator visar på motsatt föhållande. Resultatet ger en indikation på hur proportionen mellan de två grupperna kan se ut. Målet var att klassificera varje observation som antingen fotgängare eller cyklist. För att göra det har Bayesiansk statistik applicerats i form av en sannolikhetsklassifikation. Reslutatet från en klusteranalys genomförd med ”K-means clustering algorithm” användes som prior information till klassificeringsmodellen. För att kunna validera resultatet från detta ”unsupervised statistical learning” -problem, användes olika metoder för modelldiagnostik. Den valda modellen uppfyller alla krav för vad som anses vara rimligt f ̈or en stabil modell och visar tydliga tecken på konvergens. Data samlades in med Wi-Fi sensorer som upptäcker förbipasserande enheter som söker efter potentiella nätverk att koppla upp sig mot. Denna metod har visat sig inte vara den mest optimala, eftersom tillverkare idag producerar nätverkskort som genererar en slumpad adress varje gång en enhet försöker ansluta till ett nätverk. De slumpade adresserna gör det svårt att följa majoriteten av enheterna mellan sensorera, vilket gör denna typ av data olämplig för denna typ av studie. Därf ̈or föreslås att andra metoder för att samla in data används i framtiden.
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.
Texto completoTitle from PDF title page (viewed on May 14, 2010). "School of Engineering and Computer Science." Includes bibliographical references (p. 85-90).
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.
Texto completoGehrke, 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.
Texto completoThesis 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.
Ceccon, Stefano. "Extending Bayesian network models for mining and classification of glaucoma". Thesis, Brunel University, 2013. http://bura.brunel.ac.uk/handle/2438/8051.
Texto completoRuz, Heredia Gonzalo Andres. "Bayesian networks for classification, clustering, and high-dimensional data visualisation". Thesis, Cardiff University, 2008. http://orca.cf.ac.uk/54722/.
Texto completoKasai, 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.
Texto completoKalkandara, 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.
Texto completoHarmouche, 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.
Texto completoEhtiati, 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.
Texto completoROC curves depicting the scene classification performance of the two models also show that the likelihood coupling model achieves a higher detection rate compared to the prior coupling model. We have also computed the average rise times of the models' outputs as a measure of comparing the speed of the two models. The results show that the likelihood coupling model outputs have a shorter rise time. Based on these experimental findings one can conclude that imposing constraints on the likelihood models provides better solutions to the scene classification problems compared to imposing constraints on the prior models.
We have also proposed an attentional feature modulation scheme, which consists of tuning the input image responses to the bank of Gabor filters based on the scene class probabilities estimated by the model and the energy profiles of the Gabor filters for different scene categories. Experimental results based on combining the attentional feature tuning scheme with the likelihood coupling and the prior coupling methods show a significant improvement in the scene classification performances of both models.
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.
Texto completoID: 030646190; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Thesis (Ph.D.)--University of Central Florida, 2011.; Includes bibliographical references (p. 102-105).
Ph.D.
Doctorate
Mathematics
Sciences
Mathematics
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.
Texto completoOrre, 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.
Texto completoThe 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.
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 pattern recognition, hiearchical clustering and alsoused as a model for cortical memory. The learning paradigm usedin BCPNN is rather different from many other neural networkarchitectures. The learning in, e.g. the popularbackpropagation (BP) network, is a gradient method on an errorsurface, but learning in BCPNN is based upon calculations ofmarginal and joint prob- abilities between attributes. This isa quite time efficient process compared to, for instance,gradient learning. The interpretation of the weight values inBCPNN is also easy compared to many other networkarchitechtures. The values of these weights and theiruncertainty is also what we are focusing on in our data miningapplication. The most important results and findings in thisthesis can be summarised in the following points:
We demonstrate how BCPNN (Bayesian Confidence PropagationNeural Network) can be extended to model the uncertainties incollected statistics to produce outcomes as distributionsfrom two different aspects: uncertainties induced by sparsesampling, which is useful for data mining; uncertainties dueto input data distributions, which is useful for processmodelling.
We indicate how classification with BCPNN gives highercertainty than an optimal Bayes classifier and betterprecision than a naïve Bayes classifier for limited datasets.
We show how these techniques have been turned into auseful tool for real world applications within the drugsafety area in particular.
We present a simple but working method for doingautomatic temporal segmentation of data sequences as well asindicate some aspects of temporal tasks for which a Bayesianneural network may be useful.
We present a method, based on recurrent BCPNN, whichperforms a similar task as an unsupervised clustering method,on a large database with noisy incomplete data, but muchquicker, with an efficiency in finding patterns comparablewith a well known (Autoclass) Bayesian clustering method,when we compare their performane on artificial data sets.Apart from BCPNN being able to deal with really large datasets, because it is a global method working on collectivestatistics, we also get good indications that the outcomefrom BCPNN seems to have higher clinical relevance thanAutoclass in our application on the WHO database of adversedrug reactions and therefore is a relevant data mining toolto use on the WHO database.
Artificial neural network, Bayesian neural network, datamining, adverse drug reaction signalling, classification,learning.
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.
Texto completoStampoulis, 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.
Texto completoHudson, 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.
Texto completoKim, Jong Hwan. "Autonomous Navigation, Perception and Probabilistic Fire Location for an Intelligent Firefighting Robot". Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/64997.
Texto completoPh. D.
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.
Texto completoQC 20170915
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.
Texto completoTsiftsi, 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/.
Texto completoDarcy, Peter. "Resolving RFID Anomalies using Intelligent Analysis and Classification". Thesis, Griffith University, 2012. http://hdl.handle.net/10072/366922.
Texto completoThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Institute for Integrated and Intelligent Systems
Science, Environment, Engineering and Technology
Full Text
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.
Texto completoFredlund, Richard. "A Bayesian expected error reduction approach to Active Learning". Thesis, University of Exeter, 2011. http://hdl.handle.net/10036/3170.
Texto completoAmlinger, 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.
Texto completoSteckenrider, 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.
Texto completoMaster of Science
Humans have an outstanding ability to understand things about the world around them. We learn from our youngest years how to make sense of things and perceive our environment even when it is not easy. To do this, we inherently think in terms of probabilities, updating our belief as we gain new information. The methods introduced here allow an autonomous system to think similarly, by applying a fairly common probabilistic technique to the task of perception and classification. In particular, road cracks are observed and classified using these methods, in order to develop an autonomous road condition monitoring system. The results of this research are promising; cracks are identified and correctly categorized with 92% accuracy, and the additional “intelligence” of the system leads to a 33% improvement in road crack assessment. These methods could be applied in a variety of contexts as the leading edge of robotics research seeks to develop more robust and human-like ways of perceiving the world.
Severini, Jérôme. "Estimation et Classification de Signaux Altimétriques". Thesis, Toulouse, INPT, 2010. http://www.theses.fr/2010INPT0125/document.
Texto completoAfter having scanned the ocean levels during thirteen years, the french/american satelliteTopex-Poséidon disappeared in 2005. Topex-Poséidon was replaced by Jason-1 in december 2001 and a new satellit Jason-2 is waited for 2008. Several estimation methods have been developed for signals resulting from these satellites. In particular, estimators of the sea height and wave height have shown very good performance when they are applied on waveforms backscattered from ocean surfaces. However, it is a more challenging problem to extract relevant information from signals backscattered from non-oceanic surfaces such as inland waters, deserts or ices. This PhD thesis is divided into two parts : A first direction consists of developing classification methods for altimetric signals in order to recognize the type of surface affected by the radar waveform. In particular, a specific attention will be devoted to support vector machines (SVMs) and functional data analysis for this problem. The second part of this thesis consists of developing estimation algorithms appropriate to altimetric signals obtained after reflexion on non-oceanic surfaces. Bayesian algorithms are currently under investigation for this estimation problem. This PhD is co-supervised by the french society CLS (Collect Localisation Satellite) (seehttp://www.cls.fr/ for more details) which will in particular provide the real altimetric data necessary for this study
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.
Texto completoZhang, 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.
Texto completoGutiérrez, 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.
Texto completoTesis
Rebholz, Matthew John. "Dynamic Spectrum Access Network Simulation and Classification of Secondary User Properties". Thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/23244.
Texto completoHowever, DSA requires user devices to monitor large amounts of bandwidth, and the user devices are often limited in their acceptable size, weight, and power. This greatly limits the usable bandwidth when using complex channel sensing methods. Therefore, this thesis focuses on energy detection for channel sensing.
Constraining computing requirements by operating with limited spectrum sensing equipment allows for efficient use of limited broadband by user devices. The research on using the Naïve Bayesian classifier coupled with energy detection and the UDNS serves as a strong starting point for supplementary work in the area of radio classification.
Master of Science
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.
Texto completoNapier, 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/.
Texto completoZens, 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.
Texto completoClark, Andrew Robert James. "Multi-objective ROC learning for classification". Thesis, University of Exeter, 2011. http://hdl.handle.net/10036/3530.
Texto completoTang, 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.
Texto completoAli, Khan Syed Irteza. "Classification using residual vector quantization". Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50300.
Texto completoRenaud, 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.
Texto completoCao, Feng. "Classification, detection and prediction of adverse and anomalous events in medical robots". Case Western Reserve University School of Graduate Studies / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=case1339166738.
Texto completoAvcioglu-Ayturk, Mubeccel Didem. "A simulation of Industry and occupation codes in 1970 and 1980 U.S Census". Link to electronic thesis, 2005. http://www.wpi.edu/Pubs/ETD/Available/etd-060105-161730/.
Texto completo