Bibliografías temáticas / Bayesian classification

Literatura académica sobre el tema "Bayesian classification"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 20 de febrero de 2023

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Artículos de revistas sobre el tema "Bayesian classification"

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Yazdi, Hadi Sadoghi, Mehri Sadoghi Yazdi y Abedin Vahedian. "Fuzzy Bayesian Classification of LR Fuzzy Numbers". International Journal of Engineering and Technology 1, n.º 5 (2009): 415–23. http://dx.doi.org/10.7763/ijet.2009.v1.78.

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Wang, ShuangCheng, GuangLin Xu y RuiJie Du. "Restricted Bayesian classification networks". Science China Information Sciences 56, n.º 7 (9 de enero de 2013): 1–15. http://dx.doi.org/10.1007/s11432-012-4729-x.

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Berrett, Candace y Catherine A. Calder. "Bayesian spatial binary classification". Spatial Statistics 16 (mayo de 2016): 72–102. http://dx.doi.org/10.1016/j.spasta.2016.01.004.

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Dojer, Norbert, Paweł Bednarz, Agnieszka Podsiadło y Bartek Wilczyński. "BNFinder2: Faster Bayesian network learning and Bayesian classification". Bioinformatics 29, n.º 16 (1 de julio de 2013): 2068–70. http://dx.doi.org/10.1093/bioinformatics/btt323.

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Reguzzoni, M., F. Sansò, G. Venuti y P. A. Brivio. "Bayesian classification by data augmentation". International Journal of Remote Sensing 24, n.º 20 (enero de 2003): 3961–81. http://dx.doi.org/10.1080/0143116031000103817.

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Wang, Xiaohui, Shubhankar Ray y Bani K. Mallick. "Bayesian Curve Classification Using Wavelets". Journal of the American Statistical Association 102, n.º 479 (septiembre de 2007): 962–73. http://dx.doi.org/10.1198/016214507000000455.

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Williams, C. K. I. y D. Barber. "Bayesian classification with Gaussian processes". IEEE Transactions on Pattern Analysis and Machine Intelligence 20, n.º 12 (1998): 1342–51. http://dx.doi.org/10.1109/34.735807.

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Dellaportas, Petros. "Bayesian classification of Neolithic tools". Journal of the Royal Statistical Society: Series C (Applied Statistics) 47, n.º 2 (28 de junio de 2008): 279–97. http://dx.doi.org/10.1111/1467-9876.00112.

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Miguel Hernández-Lobato, Jose, Daniel Hernández-Lobato y Alberto Suárez. "Network-based sparse Bayesian classification". Pattern Recognition 44, n.º 4 (abril de 2011): 886–900. http://dx.doi.org/10.1016/j.patcog.2010.10.016.

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Hunter, L. y D. J. States. "Bayesian classification of protein structure". IEEE Expert 7, n.º 4 (agosto de 1992): 67–75. http://dx.doi.org/10.1109/64.153466.

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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.

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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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Libros sobre el tema "Bayesian classification"

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T, Denison David G., ed. Bayesian methods for nonlinear classification and regression. Chichester, England: Wiley, 2002.

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PAC-Bayesian supervised classification: The thermodynamics of statistical learning. Beachwood, Ohio: Institute of Mathematical Statistics, 2007.

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Frey, Brendan J. Bayesian networks for pattern classification, data compression, and channel coding. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1997.

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Neal, Radford M. Monte Carlo implementation of Gaussian process models for Bayesian regression and classification. Toronto: University of Toronto, 1997.

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Press, S. James. Bayesian statistics: Principles, models, and applications. New York: Wiley, 1989.

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Wang, Jun. A Bayesian classifier based on a deterministic annealing neural network for aircraft fault classification. Wright-Patterson AFB, OH: Human Resources Directorate, Logistics Research Division, U.S. Air Force Armstrong Laboratory, 1997.

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Abkar, Ali Akbar. Likelihood-based segmentation and classification of remotely sensed images: A Bayesian optimization approach for combining RS and GIS. Enschede, The Netherlands: International Institute for Aerospace Survey and Earth Sciences, 1999.

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John, Stutz, Cheeseman Peter y Ames Research Center. Artificial Intelligence Research Branch., eds. Bayesian classification theory. Moffett Field, CA: NASA Ames Research Center, Artificial Intelligence Research Branch, 1991.

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Dalton, Lori A. y Edward R. Dougherty. Optimal Bayesian Classification. SPIE, 2020.

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Dalton, Lori A. y Edward R. Dougherty. Optimal Bayesian Classification. SPIE, 2020. http://dx.doi.org/10.1117/3.2540669.

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Capítulos de libros sobre el tema "Bayesian classification"

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Zhang, Dengsheng. "Bayesian Classification". En Texts in Computer Science, 161–78. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17989-2_7.

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Hsu, Wynne. "Bayesian Classification". En Encyclopedia of Database Systems, 1–5. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_556-2.

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Hsu, Wynne. "Bayesian Classification". En Encyclopedia of Database Systems, 210–14. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_556.

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Zhang, Dengsheng. "Bayesian Classification". En Texts in Computer Science, 183–200. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69251-3_7.

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Hsu, Wynne. "Bayesian Classification". En Encyclopedia of Database Systems, 263–67. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_556.

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Almond, Russell G. y Juan-Diego Zapata-Rivera. "Bayesian Networks". En Handbook of Diagnostic Classification Models, 81–106. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05584-4_4.

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Sebastiani, Paola y Marco Ramoni. "Robust Bayesian classification". En COMPSTAT, 445–50. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-642-57678-2_61.

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Winkler, Gerhard. "Bayesian Texture Classification". En Image Analysis, Random Fields and Markov Chain Monte Carlo Methods, 243–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55760-6_17.

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Koch, Karl-Rudolf. "Classification". En Bayesian Inference with Geodetic Applications, 135–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/bfb0048714.

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Bernardo, José M. "Bayesian Linear Probabilistic Classification". En Statistical Decision Theory and Related Topics IV, 151–62. New York, NY: Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4613-8768-8_19.

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Actas de conferencias sobre el tema "Bayesian classification"

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Rodríguez-Teja, Federico, Carlos Martinez-Cagnazzo y Eduardo Grampín Castro. "Bayesian classification". En the 6th International Wireless Communications and Mobile Computing Conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1815396.1815572.

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Chakrabarty, Dalia y Coryn A. L. Bailer-Jones. "A Novel Bayesian Mass Determination Algorithm". En CLASSIFICATION AND DISCOVERY IN LARGE ASTRONOMICAL SURVEYS: Proceedings of the International Conference: “Classification and Discovery in Large Astronomical Surveys”. AIP, 2008. http://dx.doi.org/10.1063/1.3059070.

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Richards, Gordon T. y Coryn A. L. Bailer-Jones. "Bayesian Quasar Selection and the Quasar Luminosity Function". En CLASSIFICATION AND DISCOVERY IN LARGE ASTRONOMICAL SURVEYS: Proceedings of the International Conference: “Classification and Discovery in Large Astronomical Surveys”. AIP, 2008. http://dx.doi.org/10.1063/1.3059053.

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Piro, Paolo, Richard Nock, Frank Nielsen y Michel Barlaud. "Boosting Bayesian MAP Classification". En 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.167.

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Keren, Carmit, Miriam Zacksenhouse y Yakov Ben-Haim. "Info Gap Bayesian Classification". En ASME 2008 9th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2008. http://dx.doi.org/10.1115/esda2008-59188.

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Optimal decision methods and most notably the Bayesian decision are sensitive to uncertainty in the statistics of the patterns to be classified. Errors in the associated probabilities and distributions would degrade the performance of these methods. We present here a robust-satisficing decision-rule whose robustness to uncertainty in the priors is maximized given a performance demand. We apply the method to a two-class medical classification problem. We show that the robust-satisficing decision-rule is more robust to uncertainty in the priors than the optimal Bayesian decision-rule at sub-optimal performance levels. We present 4 propositions which characterize the robust-satisficing classifier. In addition we demonstrate the capabilities of this method when applied to uncertainty in the conditional probability density functions.
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"LEUKOCYTES CLASSIFICATION USING BAYESIAN NETWORKS". En 3rd International Conference on Agents and Artificial Intelligence. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003197706810684.

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Sonneland, L., P. Tennebo, T. Gehrmann y O. Yrke. "3D Model-based Bayesian classification". En 56th EAEG Meeting. European Association of Geoscientists & Engineers, 1994. http://dx.doi.org/10.3997/2214-4609.201410086.

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Mukhopadhyay, Subhadeep, Faming Liang, Paul M. Goggans y Chun-Yong Chan. "Bayesian Analysis of High Dimensional Classification". En BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: The 29th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2009. http://dx.doi.org/10.1063/1.3275621.

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Ji Won Yoon, Stephen J. Roberts, Matt Dyson y John Q. Gan. "Sequential Bayesian estimation for adaptive classification". En 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008). IEEE, 2008. http://dx.doi.org/10.1109/mfi.2008.4648010.

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Bulo, Samuel Rota y Peter Kontschieder. "Online Learning with Bayesian Classification Trees". En 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016. http://dx.doi.org/10.1109/cvpr.2016.432.

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Informes sobre el tema "Bayesian classification"

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Zhan, Zhijun, LiWu Chang y Stan Matwin. Privacy-Preserving Naive Bayesian Classification. Fort Belvoir, VA: Defense Technical Information Center, enero de 2004. http://dx.doi.org/10.21236/ada464290.

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Barker, Kash, Theodore B. Trafalis y Cameron A. MacKenzie. Bayesian Kernel Methods for Non-Gaussian Distributions: Binary and Multi-class Classification Problems. Fort Belvoir, VA: Defense Technical Information Center, mayo de 2013. http://dx.doi.org/10.21236/ada595533.

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Yeung, Ka Y., Roger E. Bumgarner y Adrian E. Raftery. Bayesian Model Averaging: Development of an Improved Multi-Class, Gene Selection and Classification Tool for Microarray Data. Fort Belvoir, VA: Defense Technical Information Center, octubre de 2004. http://dx.doi.org/10.21236/ada454826.

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Tian, Cong, Jianlong Shu, Wenhui Shao, Zhengxin Zhou, Huayang Guo y Jingang Wang. The efficacy and safety of IL Inhibitors, TNF-α Inhibitors, and JAK Inhibitor on ankylosing spondylitis: A Bayesian network meta-analysis of a “randomized, double-blind, placebo-controlled” trials. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, septiembre de 2022. http://dx.doi.org/10.37766/inplasy2022.9.0117.

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Review question / Objective: In this study, we conducted a Bayesian network meta-analysis to evaluate the efficacy and safety of interleukin (IL) inhibitors, tumor necrosis factor-alpha (TNF-α) inhibitors, and Janus kinase (JAK) inhibitors on ankylosing spondylitis (AS).The purpose of this study is to compare the effectiveness and safety of different interventions for treating AS to provide insights into the decision-making in clinicalpractice. Condition being studied: Ankylosing spondylitis. Based on the Bayesian hierarchical model, we conducted a network meta-analysis using the gemtc package in R software (version 4.1.3) and Stata software (version 15.1). Cong Tian and Jianlong Shu contributed to the conception and design of the study and supervised the tweet classification. All authors drafted the manuscript. Wenhui Shao, Zhengxin Zhou, Huayang Guo and Jingang Wang contributed to data management and tweet classification. Cong Tian, Jianlong Shu and Zhengxin Zhou performed the statistical analysis. Cong Tian, Jianlong Shu, Wenhui Shao and Zhengxin Zhou reviewed the manuscript.
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Kingston, A. W., A. Mort, C. Deblonde y O H Ardakani. Hydrogen sulfide (H2S) distribution in the Triassic Montney Formation of the Western Canadian Sedimentary Basin. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/329797.

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The Montney Formation is a highly productive hydrocarbon reservoir with significant reserves of hydrocarbon gases and liquids making it of great economic importance to Canada. However, high concentrations of hydrogen sulfide (H2S) have been encountered during exploration and development that have detrimental effects on environmental, health, and economics of production. H2S is a highly toxic and corrosive gas and therefore it is essential to understand the distribution of H2S within the basin in order to enhance identification of areas with a high risk of encountering elevated H2S concentrations in order to mitigate against potential negative impacts. Gas composition data from Montney wells is routinely collected by operators for submission to provincial regulators and is publicly available. We have combined data from Alberta (AB) and British Columbia (BC) to create a basin-wide database of Montney H2S concentrations. We then used an iterative quality control and quality assurance process to produce a dataset that best represents gas composition in reservoir fluids. This included: 1) designating gas source formation based on directional surveys using a newly developed basin-wide 3D model incorporating AGS's Montney model of Alberta with a model in BC, which removes errors associated with reported formations; 2) removed injection and disposal wells; 3) assessed wells with the 50 highest H2S concentrations to determine if gas composition data is accurate and reflective of reservoir fluid chemistry; and 4) evaluated spatially isolated extreme values to ensure data accuracy and prevent isolated highs from negatively impacting data interpolation. The resulting dataset was then used to calculate statistics for each x, y location to input into the interpolation process. Three interpolations were constructed based on the associated phase classification: H2S in gas, H2S in liquid (C7+), and aqueous H2S. We used Empirical Bayesian Kriging interpolation to generate H2S distribution maps along with a series of model uncertainty maps. These interpolations illustrate that H2S is heterogeneously distributed across the Montney basin. In general, higher concentrations are found in AB compared with BC with the highest concentrations in the Grande Prairie region along with several other isolated region in the southeastern portion of the basin. The interpolations of H2S associated with different phases show broad similarities. Future mapping research will focus on subdividing intra-Montney sub-members plus under- and overlying strata to further our understanding of the role migration plays in H2S distribution within the Montney basin.
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