Relevant bibliographies by topics / Bayesian classification

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Bayesian classification'

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

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Journal articles on the topic "Bayesian classification"

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Yazdi, Hadi Sadoghi, Mehri Sadoghi Yazdi, and Abedin Vahedian. "Fuzzy Bayesian Classification of LR Fuzzy Numbers." International Journal of Engineering and Technology 1, no. 5 (2009): 415–23. http://dx.doi.org/10.7763/ijet.2009.v1.78.

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Wang, ShuangCheng, GuangLin Xu, and RuiJie Du. "Restricted Bayesian classification networks." Science China Information Sciences 56, no. 7 (2013): 1–15. http://dx.doi.org/10.1007/s11432-012-4729-x.

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

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Dojer, Norbert, Paweł Bednarz, Agnieszka Podsiadło, and Bartek Wilczyński. "BNFinder2: Faster Bayesian network learning and Bayesian classification." Bioinformatics 29, no. 16 (2013): 2068–70. http://dx.doi.org/10.1093/bioinformatics/btt323.

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Reguzzoni, M., F. Sansò, G. Venuti, and P. A. Brivio. "Bayesian classification by data augmentation." International Journal of Remote Sensing 24, no. 20 (2003): 3961–81. http://dx.doi.org/10.1080/0143116031000103817.

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Wang, Xiaohui, Shubhankar Ray, and Bani K. Mallick. "Bayesian Curve Classification Using Wavelets." Journal of the American Statistical Association 102, no. 479 (2007): 962–73. http://dx.doi.org/10.1198/016214507000000455.

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Williams, C. K. I., and D. Barber. "Bayesian classification with Gaussian processes." IEEE Transactions on Pattern Analysis and Machine Intelligence 20, no. 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, no. 2 (2008): 279–97. http://dx.doi.org/10.1111/1467-9876.00112.

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Miguel Hernández-Lobato, Jose, Daniel Hernández-Lobato, and Alberto Suárez. "Network-based sparse Bayesian classification." Pattern Recognition 44, no. 4 (2011): 886–900. http://dx.doi.org/10.1016/j.patcog.2010.10.016.

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Hunter, L., and D. J. States. "Bayesian classification of protein structure." IEEE Expert 7, no. 4 (1992): 67–75. http://dx.doi.org/10.1109/64.153466.

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

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

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

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

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

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

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

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

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

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Books on the topic "Bayesian classification"

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John, Stutz, Cheeseman Peter, and Ames Research Center. Artificial Intelligence Research Branch., eds. Bayesian classification theory. NASA Ames Research Center, Artificial Intelligence Research Branch, 1991.

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

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Frey, Brendan J. Bayesian networks for pattern classification, data compression, and channel coding. 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. University of Toronto, 1997.

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

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Wang, Jun. A Bayesian classifier based on a deterministic annealing neural network for aircraft fault classification. 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. International Institute for Aerospace Survey and Earth Sciences, 1999.

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Bondarenko, Natal'ya. Pattern recognition. The initial course of theory. INFRA-M Academic Publishing LLC., 2024. http://dx.doi.org/10.12737/2111834.

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This tutorial discusses the tasks of pattern recognition, discriminant analysis, taxonomy, comparison with a reference, classification of features, and selection of a feature space. The main groups of features calculated from images and used for their recognition have been studied. The methods of classification based on comparison with the standard, the Bayesian classifier and decision trees are highlighted. Meets the requirements of the federal state educational standards of higher education of the latest generation. For students studying in the field of information technology, applied mathem
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Dalton, Lori A., and Edward R. Dougherty. Optimal Bayesian Classification. SPIE, 2020. http://dx.doi.org/10.1117/3.2540669.

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

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Book chapters on the topic "Bayesian classification"

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

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

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

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

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

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Sebastiani, Paola, and Marco Ramoni. "Robust Bayesian classification." In COMPSTAT. 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." In Image Analysis, Random Fields and Markov Chain Monte Carlo Methods. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55760-6_17.

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

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

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Conference papers on the topic "Bayesian classification"

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

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Dunn, Marina, Aleksandra Ciprijanovic, Brian Nord, and Bahram Mobasher. "Galaxy Morphology Classification Using Bayesian Neural Networks for LSST." In Galaxy Morphology Classification Using Bayesian Neural Networks for LSST. US DOE, 2023. http://dx.doi.org/10.2172/1969686.

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Chakrabarty, Dalia, and Coryn A. L. Bailer-Jones. "A Novel Bayesian Mass Determination Algorithm." In 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., and Coryn A. L. Bailer-Jones. "Bayesian Quasar Selection and the Quasar Luminosity Function." In 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, and Michel Barlaud. "Boosting Bayesian MAP Classification." In 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, and Yakov Ben-Haim. "Info Gap Bayesian Classification." In 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-opti
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"LEUKOCYTES CLASSIFICATION USING BAYESIAN NETWORKS." In 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, and O. Yrke. "3D Model-based Bayesian classification." In 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, and Chun-Yong Chan. "Bayesian Analysis of High Dimensional Classification." In 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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Center, Julian L. "Semi-Supervised Learning for Bayesian Pattern Classification." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 25th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2005. http://dx.doi.org/10.1063/1.2149833.

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Reports on the topic "Bayesian classification"

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Zhan, Zhijun, LiWu Chang, and Stan Matwin. Privacy-Preserving Naive Bayesian Classification. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada464290.

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

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Tian, Cong, Jianlong Shu, Wenhui Shao, Zhengxin Zhou, Huayang Guo та 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, 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
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Yeung, Ka Y., Roger E. Bumgarner, and Adrian E. Raftery. Bayesian Model Averaging: Development of an Improved Multi-Class, Gene Selection and Classification Tool for Microarray Data. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada454826.

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Wilson, D., Steven Peckham, Max Krackow, Sora Haley, Sophia Bragdon, and Jay Clausen. Discriminating buried munitions based on physical models for their thermal response. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49749.

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Munitions and other objects buried near the Earth’s surface can often be recognized in infrared imagery because their thermal and radiative properties differ from the surrounding undisturbed soil. However, the evolution of the thermal signature over time is subject to many complex interacting processes, including incident solar radiation, heat conduction in the ground, longwave radiation from the surface, and sensible and latent heat exchanges with the atmosphere. This complexity makes development of robust classification algorithms particularly challenging. Machine-learning algorithms, althou
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Kingston, A. W., A. Mort, C. Deblonde, and 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 concentratio
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Swan, Megan, and Christopher Calvo. Site characterization and change over time in semi-arid grassland and shrublands at three parks?Chaco Culture National Historic Park, Petrified Forest National Park, and Wupatki National Monument: Upland vegetation and soils monitoring 2007?2021. National Park Service, 2024. http://dx.doi.org/10.36967/2301582.

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This report presents results of upland vegetation and soil monitoring of semi-arid grasslands at three Parks by the Southern Colorado Plateau Inventory and Monitoring Network (SCPN) from 2007?2021. The purpose is to compare and contrast five grassland ecological sites and examine how they have changed during the first 15 years of monitoring. Crews collected data on composition and abundance of vegetation, both at the species level and by lifeform (e.g., perennial grass, shrub, forb) and soil aggregate stability and soil texture at 150 plots within five target grassland/shrubland communities de
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