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Artykuły w czasopismach na temat „Bayesian classification”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 25 lipca 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Gupal, A. M., S. V. Pashko, and I. V. Sergienko. "Efficiency of Bayesian classification procedure." Cybernetics and Systems Analysis 31, no. 4 (1995): 543–54. http://dx.doi.org/10.1007/bf02366409.

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12

Martinez, Matthew, Phillip L. De Leon, and David Keeley. "Bayesian classification of falls risk." Gait & Posture 67 (January 2019): 99–103. http://dx.doi.org/10.1016/j.gaitpost.2018.09.028.

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13

Baram, Yoram. "Bayesian classification by iterated weighting." Neurocomputing 25, no. 1-3 (1999): 73–79. http://dx.doi.org/10.1016/s0925-2312(98)00110-6.

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14

Lee, Michael D. "Bayesian outcome-based strategy classification." Behavior Research Methods 48, no. 1 (2015): 29–41. http://dx.doi.org/10.3758/s13428-014-0557-9.

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15

Davig, Troy, and Aaron Smalter Hall. "Recession forecasting using Bayesian classification." International Journal of Forecasting 35, no. 3 (2019): 848–67. http://dx.doi.org/10.1016/j.ijforecast.2018.08.005.

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16

Shaarawy, Samir, and Ahmed Haroun. "Bayesian Classification with Arma Sources." Egyptian Statistical Journal 38, no. 2 (1994): 165–76. http://dx.doi.org/10.21608/esju.1994.314823.

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17

Ershadi, Mohammad Mahdi, and Abbas Seifi. "An efficient Bayesian network for differential diagnosis using experts' knowledge." International Journal of Intelligent Computing and Cybernetics 13, no. 1 (2020): 103–26. http://dx.doi.org/10.1108/ijicc-10-2019-0112.

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PurposeThis study aims to differential diagnosis of some diseases using classification methods to support effective medical treatment. For this purpose, different classification methods based on data, experts’ knowledge and both are considered in some cases. Besides, feature reduction and some clustering methods are used to improve their performance.Design/methodology/approachFirst, the performances of classification methods are evaluated for differential diagnosis of different diseases. Then, experts' knowledge is utilized to modify the Bayesian networks' structures. Analyses of the results s

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18

Xu, Shuo. "Bayesian Naïve Bayes classifiers to text classification." Journal of Information Science 44, no. 1 (2016): 48–59. http://dx.doi.org/10.1177/0165551516677946.

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Text classification is the task of assigning predefined categories to natural language documents, and it can provide conceptual views of document collections. The Naïve Bayes (NB) classifier is a family of simple probabilistic classifiers based on a common assumption that all features are independent of each other, given the category variable, and it is often used as the baseline in text classification. However, classical NB classifiers with multinomial, Bernoulli and Gaussian event models are not fully Bayesian. This study proposes three Bayesian counterparts, where it turns out that classica

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19

Puspitowati, Yenita Endah, Mumun Nurwilawati, and Daniel Swanjaya. "Klasifikasi Siswa Menggunakan Bayesian Classification Di Uptd Smp Negeri 2 Baron." Melek IT : Information Technology Journal 1, no. 1 (2021): 15–22. https://doi.org/10.30742/melekitjournal.v1i1.36.

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To provide learning for students is not enough only in academics , but also needed in non- academic fields such as counseling . With counseling , psychological known how the students with what they are experiencing , so it takes the handling of student counseling . Giving questionnaire or questionnaires to students is a way to find out the problems in a natural student . This research is the development of the students' classification problems using Bayesian classification . Data obtained using a questionnaire that has been converted into a computerized , then weighted and processed using a Ba

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20

Siagian, Novriadi Antonius, Sutarman Wage, and Sawaluddin. "Dataset Weighting Features Using Gain Ratio To Improve Method Accuracy Naïve Bayesian Classification." IOP Conference Series: Earth and Environmental Science 748, no. 1 (2021): 012034. http://dx.doi.org/10.1088/1755-1315/748/1/012034.

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Abstract The Naïve Bayes method is proven to have a high speed when applied to large datasets, but the Naïve Bayes method has weaknesses when selecting attributes because Naïve Bayes is a statistical classification method that is only based on the Bayes theorem so that it can only be used to predict the probability of the class membership of a class independently. Independent without being able to do the selection of attributes that have a high correlation and correlation between one attribute with other attributes so that it can affect the value of accuracy. Naïve Bayesian Weight has been abl

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21

Reddy, Soma Datta, and Sunitha Palissery. "Uncertainty-Aware Seismic Signal Discrimination using Bayesian Convolutional Neural Networks." International Journal on Cybernetics & Informatics 13, no. 5 (2024): 207–18. http://dx.doi.org/10.5121/ijci.2024.130513.

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Seismic signal classification plays a crucial role in mitigating the impact of seismic events on human lives and infrastructure. Traditional methods in seismic hazard assessment often overlook the inherent uncertainties associated with the prediction of this complex geological phenomenon. This work introduces a probabilistic framework that leverages Bayesian principles to model and quantify uncertainty in seismic signal classification by applying a Bayesian Convolutional Neural Network (BCNN). The BCNN was trained on a dataset that comprises waveforms detected in the Southern California region

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22

DEL ÁGUILA, ISABEL MARÍA, and JOSÉ DEL SAGRADO. "REQUIREMENT RISK LEVEL FORECAST USING BAYESIAN NETWORKS CLASSIFIERS." International Journal of Software Engineering and Knowledge Engineering 21, no. 02 (2011): 167–90. http://dx.doi.org/10.1142/s0218194011005219.

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Requirement engineering is a key issue in the development of a software project. Like any other development activity it is not without risks. This work is about the empirical study of risks of requirements by applying machine learning techniques, specifically Bayesian networks classifiers. We have defined several models to predict the risk level for a given requirement using three dataset that collect metrics taken from the requirement specifications of different projects. The classification accuracy of the Bayesian models obtained is evaluated and compared using several classification perform

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23

Šaltytė, J., and K. Dučinskas. "Comparison of Nonlinear Spatial Correlation Models by the Influence of the Data Augmentation to the Classification Risk." Nonlinear Analysis: Modelling and Control 7, no. 1 (2002): 31–42. http://dx.doi.org/10.15388/na.2002.7.1.15200.

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The Bayesian classification rule used for the classification of the observations of the (second-order) stationary Gaussian random fields with different means and common factorised covariance matrices is investigated. The influence of the observed data augmentation to the Bayesian risk is examined for three different nonlinear widely applicable spatial correlation models. The explicit expression of the Bayesian risk for the classification of augmented data is derived. Numerical comparison of these models by the variability of Bayesian risk in case of the first-order neighbourhood scheme is perf

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24

Long, Yuqi, and Xingzhong Xu. "Bayesian decision rules to classification problems." Australian & New Zealand Journal of Statistics 63, no. 2 (2021): 394–415. http://dx.doi.org/10.1111/anzs.12325.

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25

Mittal, Amit Kumar, Shivangi Mittal, and Digendra Singh Rathore. "Bayesian Classification for Social Media Text." International Journal of Computer Sciences and Engineering 6, no. 7 (2018): 641–46. http://dx.doi.org/10.26438/ijcse/v6i7.641646.

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26

dos Santos, Edimilson B., Estevam R. Hruschka, Eduardo R. Hruschka, and Nelson F. F. Ebecken. "Bayesian network classifiers: Beyond classification accuracy." Intelligent Data Analysis 15, no. 3 (2011): 279–98. http://dx.doi.org/10.3233/ida-2010-0468.

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27

Ruiz, Pablo, Javier Mateos, Gustavo Camps-Valls, Rafael Molina, and Aggelos K. Katsaggelos. "Bayesian Active Remote Sensing Image Classification." IEEE Transactions on Geoscience and Remote Sensing 52, no. 4 (2014): 2186–96. http://dx.doi.org/10.1109/tgrs.2013.2258468.

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28

Cruz-Mesía, Rolando De la, Fernando A. Quintana, and Peter Müller. "Semiparametric Bayesian classification with longitudinal markers." Journal of the Royal Statistical Society: Series C (Applied Statistics) 56, no. 2 (2007): 119–37. http://dx.doi.org/10.1111/j.1467-9876.2007.00569.x.

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29

Lin, Tein-Hsiang, and Kang G. Shin. "A Bayesian approach to fault classification." ACM SIGMETRICS Performance Evaluation Review 18, no. 1 (1990): 58–66. http://dx.doi.org/10.1145/98460.98505.

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30

Akhtar, Naveed, Faisal Shafait, and Ajmal Mian. "Discriminative Bayesian Dictionary Learning for Classification." IEEE Transactions on Pattern Analysis and Machine Intelligence 38, no. 12 (2016): 2374–88. http://dx.doi.org/10.1109/tpami.2016.2527652.

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31

Hurn, M. A., K. V. Mardia, T. J. Hainsworth, J. Kirkbride, and E. Berry. "Bayesian fused classification of medical images." IEEE Transactions on Medical Imaging 15, no. 6 (1996): 850–58. http://dx.doi.org/10.1109/42.544502.

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32

Hong, Euy-Seok. "Software Quality Classification using Bayesian Classifier." Journal of the Korea society of IT services 11, no. 1 (2012): 211–21. http://dx.doi.org/10.9716/kits.2012.11.1.211.

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33

Wan, E. A. "Neural network classification: a Bayesian interpretation." IEEE Transactions on Neural Networks 1, no. 4 (1990): 303–5. http://dx.doi.org/10.1109/72.80269.

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34

Dellaportas, Patros. "Corrigendum: Bayesian classification of Neolithic tools." Journal of the Royal Statistical Society: Series C (Applied Statistics) 47, no. 4 (2002): 620. http://dx.doi.org/10.1111/1467-9876.00133.

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35

Vinay, A., Abhijay Gupta, Aprameya Bharadwaj, Arvind Srinivasan, K. N. Balasubramanya Murthy, and S. Natarajan. "Unconstrained Face Recognition using Bayesian Classification." Procedia Computer Science 143 (2018): 519–27. http://dx.doi.org/10.1016/j.procs.2018.10.425.

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36

Kabán, Ata. "On Bayesian classification with Laplace priors." Pattern Recognition Letters 28, no. 10 (2007): 1271–82. http://dx.doi.org/10.1016/j.patrec.2007.02.010.

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37

Ni, Yang, Peter Müller, Maurice Diesendruck, Sinead Williamson, Yitan Zhu, and Yuan Ji. "Scalable Bayesian Nonparametric Clustering and Classification." Journal of Computational and Graphical Statistics 29, no. 1 (2019): 53–65. http://dx.doi.org/10.1080/10618600.2019.1624366.

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38

Dadaneh, Siamak Zamani, Edward R. Dougherty, and Xiaoning Qian. "Optimal Bayesian Classification With Missing Values." IEEE Transactions on Signal Processing 66, no. 16 (2018): 4182–92. http://dx.doi.org/10.1109/tsp.2018.2847660.

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39

Klocker, Johanna, Bettina Wailzer, Gerhard Buchbauer, and Peter Wolschann. "Bayesian Neural Networks for Aroma Classification." Journal of Chemical Information and Computer Sciences 42, no. 6 (2002): 1443–49. http://dx.doi.org/10.1021/ci0202640.

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40

Zhang, Xunan, Shiji Song, and Cheng Wu. "Robust Bayesian Classification with Incomplete Data." Cognitive Computation 5, no. 2 (2012): 170–87. http://dx.doi.org/10.1007/s12559-012-9188-6.

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41

Bielza, C., G. Li, and P. Larrañaga. "Multi-dimensional classification with Bayesian networks." International Journal of Approximate Reasoning 52, no. 6 (2011): 705–27. http://dx.doi.org/10.1016/j.ijar.2011.01.007.

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42

Gutiérrez, Luis, Eduardo Gutiérrez-Peña, and Ramsés H. Mena. "Bayesian nonparametric classification for spectroscopy data." Computational Statistics & Data Analysis 78 (October 2014): 56–68. http://dx.doi.org/10.1016/j.csda.2014.04.010.

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43

Krzanowski, Wojtek J., Trevor C. Bailey, Derek Partridge, Jonathan E. Fieldsend, Richard M. Everson, and Vitaly Schetinin. "Confidence in Classification: A Bayesian Approach." Journal of Classification 23, no. 2 (2006): 199–220. http://dx.doi.org/10.1007/s00357-006-0013-3.

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44

Carhart, Gary W., Bret F. Draayer, and Michael K. Giles. "Optical pattern recognition using bayesian classification." Pattern Recognition 27, no. 4 (1994): 587–606. http://dx.doi.org/10.1016/0031-3203(94)90039-6.

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45

Kehagias, A. "Bayesian classification of Hidden Markov Models." Mathematical and Computer Modelling 23, no. 5 (1996): 25–43. http://dx.doi.org/10.1016/0895-7177(96)00010-6.

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46

Cazorla, M. A., and F. Escolano. "Two bayesian methods for junction classification." IEEE Transactions on Image Processing 12, no. 3 (2003): 317–27. http://dx.doi.org/10.1109/tip.2002.806242.

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47

Nykaza, Edward T., Matthew G. Blevins, Carl R. Hart, and Anton Netchaev. "Bayesian classification of environmental noise sources." Journal of the Acoustical Society of America 141, no. 5 (2017): 3522. http://dx.doi.org/10.1121/1.4987416.

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48

Flach, Peter A., and Nicolas Lachiche. "Naive Bayesian Classification of Structured Data." Machine Learning 57, no. 3 (2004): 233–69. http://dx.doi.org/10.1023/b:mach.0000039778.69032.ab.

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49

Klein, Ruben, and S. James Press. "Adaptive Bayesian Classification of Spatial Data." Journal of the American Statistical Association 87, no. 419 (1992): 844–51. http://dx.doi.org/10.1080/01621459.1992.10475287.

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50

Li, Xiuqi, and Subhashis Ghosal. "Bayesian classification of multiclass functional data." Electronic Journal of Statistics 12, no. 2 (2018): 4669–96. http://dx.doi.org/10.1214/18-ejs1522.

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