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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.
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Thesis (Ph.D. in Statistical Sciences)--S.M.U.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 (one billionth of a metre), and for ease of reference these images will henceforth be referred to as nanoimages. The focus is restricted to nanoimages, obtained using a Transmission Electron Microscope (TEM). A common phenomenon that occurs during the image capturing is occlusion of objects in the image. This occlusion leads to some unwanted results during the image analysis phase, making the use of a more sophisticated classification algorithm necessary. An automated classification algorithm that successfully deals with occluded objects in nanoimages is discussed and a detailed discussion on the implementation of this algorithm is provided. The techniques used in the algorithm involve a combination of several Bayesian techniques to classify the objects in the nanoimage. Markov Chain Monte Carlo (MCMC) sampling techniques are used to simulate the unknown posterior, with samplers ranging from the Metropolis-Hastings and Reversable Jumps MCMC samplers to Monte Carlo Metropolis Hastings samplers used in obtaining the simulated posterior. Since one of the main objectives of this investigation will be the processing of images, a discussion on the most widely used image processing techniques is provided, with specific focus on how these techniques are used to extract objects of interest from the image. An overview of nanotechnology and its applications is provided, along with a variability study for the capturing of nanoimages using TEM. The aim of the study is to introduce controlled variability in the sampling through imposing specific sampling conditions, in order to determine if imposing these conditions significantly affects the measurements obtained. This variability study, according to our knowledge, is the first performed at this level of detail, and provides very useful considerations when performing a nanoimage study.Mini Dissertaion (MSc)--University of Pretoria, 2017.
NRF (under CSUR grant 90315)
CSIR
Statistics
MSc
Unrestricted
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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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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 demonstrate how they fit into the Bayesian framework. The efficient implementation of a Gaussian process is discussed with particular reference to approximate methods for matrix inversion based on the work of Skilling (1993). Several regression problems are examined. Non-stationary covariance functions are developed for the regression of neuron spike data and the use of Gaussian processes to model the potential energy surfaces of weakly bound molecules is discussed. Classification methods based on Gaussian processes are implemented using variational methods. Existing bounds (Jaakkola and Jordan 1996) for the sigmoid function are used to tackle binary problems and multi-dimensional bounds on the softmax function are presented for the multiple class case. The performance of the variational classifier is compared with that of other methods using the CRABS and PIMA datasets (Ripley 1996) and the problem of predicting the cracking of welds based on their chemical composition is also investigated. The theoretical calculation of the density of states of crystal structures is discussed in detail. Three possible approaches to the problem are described based on free energy minimization, Gaussian processes and the theory of random matrices. Results from these approaches are compared with the state-of-the-art techniques (Pickard 1997).
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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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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 DNA sites based on Bayesian mixture modelling. Our method not only accounts for heterogeneous data but also identifies the underlying classes and enables their interpretation. We also introduce novel MCMC methodology with the same, or greater, estimation performance than existing algorithms but with lower computational cost. We find that our mixture model can successfully detect evolutionary heterogeneity and demonstrate its direct relevance by applying it to real DNA data. One of these applications is the analysis of sixteen strains of one of the bacterial species that cause Lyme disease. Results from that analysis have helped understanding the evolutionary paths of these bacterial strains and, therefore, the dynamics of the spread of Lyme disease. Our method is discussed in the context of DNA but it may be extendedto othertypesof molecular data. Moreover,the classification scheme thatwe propose is evidence of the breadth of application of mixture modelling and a step forwards in the search for more realistic models of theprocesses that underlie phylogenetic data.
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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.Includes 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.
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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, i 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 other streets showed the opposite. The goal of this thesis was to classify observations as either pedestrians or as cyclists. To do that, Bayesian statistics was applied to perform a classification. Results from a cluster analysis performed with K-means algorithm were used as prior information to a probabilistic classification model. To be able to validate the results from this unsupervised statistical learning problem, several model diagnostic methods were used. The final model passes all limits of what is considered to be a stable model and shows clear signs of convergence. The data was collected using Wi-Fi sensors which detect a device passing by when the device is searching the area for a network to connect to. This thesis will focus on data from three months. Using Wi-Fi sensors as a data collection method makes it possible to track a device. However, many manufacturers produce network interface controllers that generate randomized addresses when the device is connecting to a network, which makes it difficult to track the majority of the devices. Therefore, Wi-Fi sensor data could be seen as not suitable for this type of study. Hence it is suggested that other methods should be used in the future.I 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.
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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.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.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 obtaining sufficient measurements. This research aims to assess and extend Bayesian Network (BN) models to investigate the nature of the disease and its progression, as well as improve early diagnosis performance. The exibility of BNs and their ability to integrate with clinician expertise make them a suitable tool to effectively exploit the available data. After presenting the problem, a series of BN models for cross-sectional data classification and integration are assessed; novel techniques are then proposed for classification and modelling of glaucoma progression. The results are validated against literature, direct expert knowledge and other Artificial Intelligence techniques, indicating that BNs and their proposed extensions improve glaucoma diagnosis performance and enable new insights into the disease process.
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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 simple Bayesian network classifiers. This approach creates a completely new class of networks which previously was limited mostly to two well known models, the naive Bayesian (NB) classifier and the Tree Augmented Naive Bayes (TAN) classifier. The proposed learning algorithm enhances the NB model by adding a Bayesian monitoring system. Therefore, the complexity of the resulting network is determined according to the input data yielding structures which model the data distribution in a more realistic way which improves the classification performance. Research on Bayesian networks for clustering has not been as popular as for classification tasks. A new unsupervised learning algorithm for three types of Bayesian network classifiers, which enables them to carry out clustering tasks, is introduced. The resulting models can perform cluster assignments in a probabilistic way using the posterior probability of a data point belonging to one of the clusters. A key characteristic of the proposed clustering models, which traditional clustering techniques do not have, is the ability to show the probabilistic dependencies amongst the variables for each cluster. This feature enables a better understanding of each cluster. The final part of this thesis introduces one of the first developments for Bayesian networks to perform topographic mapping. A new unsupervised learning algorithm for the NB model is presented which enables the projection of high-dimensional data into a two-dimensional space for visualisation purposes. The Bayesian network formalism of the model allows the learning algorithm to generate a density model of the input data and the presence of a cost function to monitor the convergence during the training process. These important features are limitations which other mapping techniques have and which have been overcome in this research.
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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 Bayesian model selection, fitting the entire spectrum with Monte Carlo Markov Chain methods which allow estimation of the entire parameter posterior distributions, and hence principled statistical analysis even at low signal-to-noise. After extensive testing of SuperNovaMC against simulations and literature data, we use it to classify 20 of our Dark Energy Survey candidates as Type Ia supernovae. We further performed equivalent width measurements of two Type Ia supernova spectral features: Ca II H&K and Si II 4000, using a sub-sample of the 20 Type Ia supernovae. We compared our results to those of a similar study conducted on a low-redshift (z < 0:1) Type Ia supernova sample and found the two sets of results to be consistent, suggesting no redshift evolution in the equivalent widths of the two spectral features in the redshift range 0:1 < z < 0:3 that we conducted the study in.
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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 approaches do not model the variation of the MRI tissue intensities in the brain, so as to accurately locate lesions in the posterior fossa, where the intensities vary significantly from the rest of the brain. To this end, the posterior probability distribution is used to determine MRI voxel labels for background, cerebrospinal fluid, grey matter, white matter, as well as labels for two lesion types which differ due to their appearance on MRIs: T1-hypointense lesions (also called black holes) and T2-hyperintense lesions excluding black holes. Furthermore, the proposed method provides neuropathology experts with a confidence level in the classification, which has not been provided in previous work. Spatial variability in intensity distributions over the brain is explicitly modeled by (1) segmenting the brain into distinct anatomical regions, (2) building the likelihood distributions of each tissue class in each region and (3) modeling each distribution as a multidimensional Gaussian using intensities from multimodal MRIs. Local smoothness is enforced by incorporating Markov random fields in the prior probability and thus taking into account neighboring voxel tissue classes. Qualitative and quantitative validation is performed for both lesion classes on real data from 10 patients with multiple sclerosis. Validation on ten patients for both lesion types has not been performed by previous works. Lesion classification results are compared to classifications performed by several experts and two other automatic classification techniques, using volume count and overlap. Automatic classification results are comparable to manual classifications, thus providing a more consistent and time effective alternative to manual classification. In addition, the proposed method has the advantage of providing a more accurate classification in the posterior fossa, which is a region of the brain that is difficult to classify, and where no other automatic method reports success.
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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 exchange between the two processes has motivated us to build a computational model where object and scene classification proceed in an interdependent manner, while no hierarchical relationship is imposed between the two processes. We propose a strongly coupled data fusion model for implementing the feedback relationship between the scene and object classification processes. We present novel schemes for modifying the Bayesian solutions for the scene and object classification tasks which allow data fusion between the two modules based on the constraining of the priors or the likelihoods. We have implemented and tested the two proposed models using a database of natural images created for this purpose. The Receiver Operator Curves (ROC) depicting the scene classification performance of the likelihood coupling and the prior coupling models show that scene classification performance improves significantly in both models as a result of the strong coupling of the scene and object modules.ROC 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.
22
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 constant" values (Model 1) and another which retains the components for which between-group variations are larger than within-group variation (Model 2). We show that particular cases of the above two models recover familiar variance or ANOVA-based component selection. When one has only two classes and features are a priori independent, Model 2 reduces to the Feature Annealed Independence Rule (FAIR) introduced by Fan and Fan (2008) and can be viewed as a natural generalization to the case of L greater than] 2 classes. A nontrivial result of the dissertation is that the precision of feature selection using Model 2 improves when the number of classes grows. Subsequently, we examine the rate of misclassification with and without feature selection on the basis of Model 2.ID: 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
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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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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.
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.
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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 measurement uncertainties. Moreover, the location of the source with respect to the centre of observation and the background contamination can lead to non-detection of sources (missing data). This phenomenon becomes more apparent for low flux objects, thus indicating that the missing data mechanism is non-ignorable. In order to avoid inferential biases, it is vital that the different sources of uncertainties, po- tential bias and missing data mechanism be properly accounted for. However, the majority of the methods in the relevant literature for estimating the log(N)−log(S) curve are based on the assumption of complete surveys with non missing data. In this thesis, we present a Bayesian hierarchical model that properly accounts for the missing data mechanism and the other sources of uncertainty. More specifically, we model the joint distribution of the complete data and model parameters and then derive the posterior distribution of the model parameters marginalised across all missing data information. We utilise a Blocked Gibbs sampler in order to extract samples from the joint posterior distribution of the parameters of interest. By using a Bayesian approach, we produce a posterior distribution for the log(N) − log(S) curve instead of a best-fit estimate. We apply this method to the Chandra Deep Field South (CDFS) dataset. Furthermore, approaching this complicated problem from a fully Bayesian angle enables us to appropriately model the uncertainty about the conversion factor between observed source photon counts and observed luminosity. Using relevant spectral data for the observed sources, the uncertainty about the flux-to-count conversion factor γ for each observed source is expressed through MCMC draws from the posterior distribution of γ for each source. In order to account for this uncertainty in the non- detected sources, we develop a novel statistical approach for fitting a hierarchical prior on the flux-to-count conversion factor based on the MCMC samples from the observed sources (a statistical approach that can be used in many modelling prob- lems of similar nature). We derive in a similar manner the posterior distribution of the model parameters, marginalised across the missing data, and we explore the impact in our posterior estimates of the parameters of interest in the CDFS dataset. Studying the log(N) − log(S) relationship for different source populations can give us further insight into the differences between the various types of astronomical pop- ulations. Hence, we propose a new soft-clustering scheme for classifying galaxies in different activity classes (Star Forming Galaxies, LINERs, Seyferts and Composites) using simultaneously 4 optical emission-line ratios ([NII]/Hα, [SII]/Hα, [OI]/Hα and [OIII]/Hβ). The most widely used classification approach is based on 3 diagnostic diagrams, which are 2-dimensional projections of those emission line ratios. Those diagnostics assume fixed classification boundaries, which are developed through theoretical models. However, the use of multiple diagnostic diagrams independently of one another often gives contradicting classifications for the same galaxy, and the fact that those diagrams are 2-dimensional projections of a complex multi-dimensional space is limiting the power of those diagnostics. In contrast, we present a data- driven soft clustering scheme that estimates the posterior probability of each galaxy belonging to each activity class. More specifically, we fit a large number of multivariate Gaussian distributions to the Sloan Digital Sky Survey (SDSS) dataset in order to capture local structures and subsequently group the multivariate Gaussian distributions to represent the complex multi-dimensional structure of the joint distribution of the 4 galaxy activity classes. Finally, we discuss how this soft-clustering can lead to estimates of population-specific log(N) − log(S) relationships.
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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 avoiding obstacles in real time under cluttered and dynamic environments. In addition, research for characterizing fire environments to provide firefighting robots with proper headings that lead the robots to ultimately find the fire is incomplete.
For use on intelligent firefighting robots, this research developed a real-time local obstacle avoidance method, local dynamic goal-based fire location, appropriate feature selection for fire environment assessment, and probabilistic classification of fire, smoke and their thermal reflections. The real-time local obstacle avoidance method called the weighted vector method is developed to perceive the local environment through vectors, identify suitable obstacle avoidance modes by applying a decision tree, use weighting functions to select necessary vectors and geometrically compute a safe heading. This method also solves local obstacle avoidance problems by integrating global and local goals to reach the final goal. To locate a fire outside of the robot field of view, a local dynamic goal-based 'Seek-and-Find' fire algorithm was developed by fusing long wave infrared camera images, ultraviolet radiation sensor and Lidar. The weighted vector method was applied to avoid complex static and unexpected dynamic obstacles while moving toward the fire. This algorithm was successfully validated for a firefighting robot to autonomously navigate to find a fire outside the field of view.
An improved 'Seek-and-Find' fire algorithm was developed using Bayesian classifiers to identify fire features using thermal images. This algorithm was able to discriminate fire and smoke from thermal reflections and other hot objects, allowing the prediction of a more robust heading for the robot. To develop this algorithm, appropriate motion and texture features that can accurately identify fire and smoke from their reflections were analyzed and selected by using multi-objective genetic algorithm optimization. As a result, mean and variance of intensity, entropy and inverse difference moment in the first and second order statistical texture features were determined to probabilistically classify fire, smoke, their thermal reflections and other hot objects simultaneously. This classification performance was measured to be 93.2% accuracy based on validation using the test dataset not included in the original training dataset. In addition, the precision, recall, F-measure, and G-measure were 93.5 - 99.9% for classifying fire and smoke using the test dataset.Ph. D.
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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 has a computationally tractable expression. Applications of the CTA transition kernel are demonstrated in a sequential Monte Carlo (SMC) setting for counting the number of decomposable graphs. Paper B presents the SMC scheme in a more general setting specifically designed for approximating distributions over decomposable graphs. The transition kernel from CTA from Paper A is incorporated as proposal kernel. To improve the traditional SMC algorithm, a particle Gibbs sampler with a systematic refreshment step is further proposed. A simulation study is performed for approximate graph posterior inference within both log-linear and decomposable Gaussian graphical models showing efficiency of the suggested methodology in both cases. Paper C explores the particle Gibbs sampling scheme of Paper B for approximate posterior computations in the Bayesian predictive classification framework. Specifically, Bayesian model averaging (BMA) based on the posterior exploration of the class-specific model is incorporated into the predictive classifier to take full account of the model uncertainty. For each class, the dependence structure underlying the observed features is represented by a distribution over the space of decomposable graphs. Due to the intractability of explicit expression, averaging over the approximated graph posterior is performed. The proposed BMA classifier reveals superior performance compared to the ordinary Bayesian predictive classifier that does not account for the model uncertainty, as well as to a number of out-of-the-box classifiers. Paper D develops a novel prior distribution over DAGs with the ability to express prior knowledge in terms of graph layerings. In conjunction with the prior, a stochastic optimization algorithm based on the layering property of DAGs is developed for performing structure learning in Bayesian networks. A simulation study shows that the algorithm along with the prior has superior performance compared with existing priors when used for learning graph with a clearly layered structure.QC 20170915
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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 significant advantage of using one algorithm over the other when only considering accuracy. However, ease of use lies in Random forests’ favour because the technique requires little pre-processing of the data and still generates accurate results with few false positives.
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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 sand bodies, is more substantial and can replace ad-hoc and simplistic methods that have been used in the past. Finally, we conclude this work by extending the proposed classification algorithm for shapes in three-dimensions.
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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 being used in a
fraction of its potential applications. Persistent anomalies that exist in the captured data
sets can be classied into either false-positive or false-negative readings.
Several methodologies have been presented in the past to correct RFID anomalies,
however, due to a lack of intelligence or necessary information, the maximum integrity
is not always possible to achieve by currently used techniques. To enhance the overall
accuracy of RFID systems, this research proposes a means to correct stored RFID data
based on both intelligent analysis and classiers employed at a deferred stage of the data
capturing process. We have investigated three classiers due to their impressive performance
and novelty to conduct our investigation: a Bayesian Network, Neural Network
and Non-Monotonic Reasoning.
The main contribution of this thesis involves applying the Bayesian Network
[Darcy et al., 2009b], Neural Network [Darcy et al., 2010b] and Non-Monotonic
Reasoning [Darcy et al., 2009a, Darcy et al., 2010c] classiers to clean both false-negative
and false-positive [Darcy et al., 2011c, Darcy et al., 2012a] anomalies. From our ndings
[Darcy et al., 2011a], our proposed methodologies have improved the cleaning accuracy
of existing state-of-the-art techniques and have been found to be statistically signicant
[Darcy et al., 2007, Darcy et al., 2009c]. We have also proposed further extensions of our
approach to be applied to other domains by integrating intrusion detection into our
concept [Darcy et al., 2010a], clean common hospital scenario-driven anomalies
[Darcy et al., 2010f, Darcy et al., 2010e] then eectively transform low level observations
into high level meaningful events [Darcy et al., 2010d, Darcy et al., 2011d], and modied
the classiers to include a second level of intelligence to integrate the determination of
the three classiers [Darcy et al., 2011b, Darcy et al., 2012b].Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Institute for Integrated and Intelligent Systems
Science, Environment, Engineering and Technology
Full Text
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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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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 how new data is sampled. An expected cost reduction framework for active learning is then developed which allows the learner to sample data according to arbitrary query densities. The model makes no assumption of independence between queries, instead updating model parameters on the basis of both which observations were made \textsl{and} how they were sampled. This approach is demonstrated on the probabilistic high-low game which is a non-separable extension of the high-low game presented by \cite{Seung_etal1993}. The results indicate that the Bayes expected cost reduction approach performs significantly better than passive learning even when there is considerable overlap between the class distributions, covering $30\%$ of input space. For the probabilistic high-low game however narrow queries appear to consistently outperform wide queries. We therefore conclude the first part of the thesis by investigating whether or not this is always the case, demonstrating examples where sampling broadly is favourable to a single input query. \paragraph{} Secondly, we explore the Bayesian expected cost reduction approach to active learning within the pool-based setting. This is where learning is limited to a finite pool of unlabelled observations from which the learner may select observations to be queried for class-labels. Our implementation of this approach uses Gaussian process classification with the expectation propagation approximation to make the necessary inferences. The implementation is demonstrated on six benchmark data sets and again demonstrates superior performance to passive learning.
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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 with model parameters learned via Bayesian inference, as a demonstration of a different branch of clustering. Results are provided using Silhouettes as evaluation for geo-spatial clustering and a user study for the end classification. The results are promising as an outline for a framework of classification and activity detection, and shed lights on various pitfalls that might be encountered during implementation of such service.
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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 functions that can be integrated over class decision boundaries created by a quadratic Bayesian classifier. The proposed approach is specifically formulated for road crack detection and characterization, which is one of the potential applications. For test images assessed with this technique, ground truth was estimated accurately and consistently with effective Bayesian correction, showing a 33% improvement in recall rate over standard classification. Application to road cracks demonstrated successful detection and classification in a practical domain. The proposed approach is extremely effective in characterizing highly probabilistic features in noisy environments when several correlated observations are available either from multiple sensors or from data sequentially obtained by a single sensor.Master 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.
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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 des outils de classifications permettant d'identifier les différents types de surfaces observées. Nous proposons dans cette étude d'appliquer la méthode d'estimation bayésienne aux formes d'onde altimétriques ainsi que de nouvelles approches de classification. Nous proposons enfin la mise en place d'un algorithme spécifique permettant l'étude de la topographie en milieu côtier, étude qui est actuellement très peu développée dans le domaine de l'altimétrieAfter 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
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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. A polynomial approximation to the likelihood function allows marginalised estimates of model parameters to be obtained in the form of a Volterra series. The series can be applied directly to the observed data vector in an iterative fashion, to converge upon a set of parameter maximum a-posteriori (MAP) estimates with low computational cost. An example implementation for optical character recognition (OCR) of handwritten characters illustrates the behaviour and utility of the estimator. 3. An optimisation algorithm is proposed based on recursive ordering of the model parameter marginals for model selection. The marginals are computed using Markov chain based simulated annealing, which in itself is an effective optimisation algorithm. Results for model candidates based on highly correlated linear basis functions show that the recursive parameter ordering algorithm enhances the performance of the simulated annealer over that of simulated tempering for individual sampling runs. Optimisation based upon this algorithm can work comparatively more efficiently when the observed data is approximately locally rather than globally correlated. This advantage is demonstrated in an overlapping object recognition example where the ability of the Volterra parameter estimator to handle obscured data is also utilised.
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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 enfoques bayesianos para la estimación de prevalencia cuando no es factible tener resultados de una prueba 100% exacta.
El primero es un modelo con dos parámetros que toman en cuenta la asociación entre los
resultados de las pruebas. El segundo es un enfoque que propone el uso del Bayesian Model Averaging para combinar los resultados de cuatro modelos donde cada uno de estos tiene suposiciones diferentes sobre la asociación entre los resultados de las pruebas diagnósticas.
Ambos enfoques son estudiados mediante simulaciones para evaluar el desempeño de estos bajo diferentes escenarios. Finalmente estas técnicas serán usadas para estimar la prevalencia de enfermedad renal crónica en el Perú con datos de un estudio de cohortes de CRONICAS (Francis et al., 2015).Tesis
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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.However, 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
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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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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 and the composite model approach. The data augmentation approach treats any zero values as rounded zeros, i.e. traces of components below limits of detection, and updates those zero values with non-zero values. This is better than the simple approach of adding constant values to zeros as it reduces any artificial correlation produced by updating the zeros as part of the modelling procedure. However, due to the small detection limit it does not necessarily alleviate the problems of having a point mass very close to zero. The composite model approach treats any zero components as being absent from a composition. This is done by splitting the data into subsets according to the presence or absence of certain components to produce different data configurations that are then modelled separately. The models are applied to a database consisting of the elemental configurations of forensic glass fragments with many levels of variability and of various use types. The main purposes of the model are (i) to derive expressions for the posterior predictive probabilities of newly observed glass fragments to infer their use type (classification) and (ii) to compute the evidential value of glass fragments under two complementary propositions about their source (forensic evidence evaluation). Simulation studies using cross-validation are carried out to assess both model approaches, with both performing well at classifying glass fragments of use types bulb, headlamp and container, but less well so when classifying car and building windows. The composite model approach marginally outperforms the data augmentation approach at the classification task; both approaches have the edge over support vector machines (SVM). Both model approaches also perform well when evaluating the evidential value of glass fragments, with false negative and false positive error rates below 5%. The results from glass classification and evidence evaluation are an improvement over existing methods. Assessment of the models as part of the evidence evaluation simulation study also leads to a restriction being placed upon the reported strength of the value of this type of evidence. To prevent strong support in favour of the wrong proposition it is recommended that this glass evidence should provide, at most, moderately strong support in favour of a proposition. The classification and evidence evaluation procedures are implemented into an online web application, which outputs the corresponding results for a given set of elemental composition measurements. The web application contributes a quick and easy-to-use tool for forensic scientists that deal with this type of forensic evidence in real-life casework.
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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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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-objective evolutionary algorithm (MOEA) is used to find clas- sifiers whose ROC graph locations are Pareto optimal. The Relevance Vector Machine (RVM) is a state-of-the-art classifier that produces sparse Bayesian models, but is unfor- tunately prone to overfitting. Using the MOEA, hyper-parameters for RVM classifiers are set, optimising them not only in terms of true and false positive rates but also a novel measure of RVM complexity, thus encouraging sparseness, and producing approximations to the Pareto front. Several methods for regularising the RVM during the MOEA train- ing process are examined and their performance evaluated on a number of benchmark datasets demonstrating they possess the capability to avoid overfitting whilst producing performance equivalent to that of the maximum likelihood trained RVM. A common task in bioinformatics is to identify genes associated with various genetic conditions by finding those genes useful for classifying a condition against a baseline. Typ- ically, datasets contain large numbers of gene expressions measured in relatively few sub- jects. As a result of the high dimensionality and sparsity of examples, it can be very easy to find classifiers with near perfect training accuracies but which have poor generalisation capability. Additionally, depending on the condition and treatment involved, evaluation over a range of costs will often be desirable. An MOEA is used to identify genes for clas- sification by simultaneously maximising the area under the ROC curve whilst minimising model complexity. This method is illustrated on a number of well-studied datasets and ap- plied to a recent bioinformatics database resulting from the current InChianti population study. Many classifiers produce “hard”, non-probabilistic classifications and are trained to find a single set of parameters, whose values are inevitably uncertain due to limited available training data. In a Bayesian framework it is possible to ameliorate the effects of this parameter uncertainty by averaging over classifiers weighted by their posterior probabil- ity. Unfortunately, the required posterior probability is not readily computed for hard classifiers. In this thesis an Approximate Bayesian Computation Markov Chain Monte Carlo algorithm is used to sample model parameters for a hard classifier using the AUC as a measure of performance. The ability to produce ROC curves close to the Bayes op- timal ROC curve is demonstrated on a synthetic dataset. Due to the large numbers of sampled parametrisations, averaging over them when rapid classification is needed may be impractical and thus methods for producing sparse weightings are investigated.
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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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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 set, a reasonably large training set can make the implementation cost of the nearest neighbor classifier unfeasibly costly. Although, the k-d tree structure offers a far more efficient implementation of 1-NN search, however, the cost of storing the data points can become prohibitive, especially in higher dimensionality.
RVQ also offers a nice solution to a cost-effective implementation of 1-NN-based classification. Because of the direct-sum structure of the RVQ codebook, the memory and computational of cost 1-NN-based system is greatly reduced. Although, as compared to an equivalent 1-NN system, the multi-stage implementation of the RVQ codebook compromises the accuracy of the class boundaries, yet the classification error has been empirically shown to be within 3% to 4% of the performance of an equivalent 1-NN-based classifier.
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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 using known genomic variants to measure empirical error rates. However, for this heuristic to work, an adequate characterization of the variants present in a population must be available -which means that this approach is not possible for a wide range of species.
This thesis develops methods to directly produce error probabilities that are representative of their empirical error rates for raw sequencing data. These can then be incorporated into Bayesian maximum a posteriori algorithms to make highly accurate inferences about the likelihood of the model that gave rise to this observed data. First, an algorithm to produce highly accurate nucleotide basecalls along with calibrated error probabilities is presented. Using the resulting data, individual reads can be robustly as- signed to their samples of origin and ancient DNA fragments can be inferred even at high error rates. For archaic hominin samples, the number of DNA fragments from present-day human contamination can also be accurately quantified.
The novel algorithms developed during the course of this thesis provide an alternative approach to working with Illumina sequence data. They also provide a demonstrable improvement over existing computational methods for basecalling, inferring ancient DNA fragments, demultiplexing, and estimating present-day human contamination along with reconstruction of mitochondrial genomes in ancient hominins.
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Cao, 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.
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Avcioglu-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/.
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