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Artykuły w czasopismach na temat „Classification probabiliste”

Autor: Grafiati

Data publikacji: 12 października 2024

Data aktualizacji: 31 lipca 2025

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1

Mselati, Benoit. "Classification et représentation probabiliste des solutions positives d'une équation elliptique semi-linéaire." Comptes Rendus Mathematique 335, no. 9 (2002): 733–38. http://dx.doi.org/10.1016/s1631-073x(02)02557-8.

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Garbolino, Emmanuel, Patrice De Ruffray, Henry Brisse, and Gilles Grandjouan. "Les phytoclimats de France : classification probabiliste de 1874 bio-indicateurs du climat." Comptes Rendus Biologies 331, no. 11 (2008): 881–95. http://dx.doi.org/10.1016/j.crvi.2008.08.009.

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Stan, Emanuela, Camelia-Oana Muresan, Raluca Dumache, et al. "Sex Estimation from Computed Tomography of Os Coxae—Validation of the Diagnose Sexuelle Probabiliste (DSP) Software in the Romanian Population." Applied Sciences 14, no. 10 (2024): 4136. http://dx.doi.org/10.3390/app14104136.

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This study aimed to evaluate the DSP method’s applicability to Romania’s contemporary population and to assess the accuracy and reliability of variables derived from CT images. A total of 80 pelvic CT scans were analyzed. Participants ranged from 22 to 93 years, with a mean age of 59.51 ± 22.7 years. All variables measured from the CT scans were analyzed using DSP software. The study found that sex estimation was possible in 71.25% of cases overall, with varying rates between males (57.50%) and females (85%). Despite encountering undetermined specimens comprising 42.5% males and 15% females, o

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Gamboa, Luis Fernando. "Strategic Uses of Mobile Phones in the BoP: Some Examples in Latin American Countries." Lecturas de Economía, no. 71 (February 23, 2010): 209–34. http://dx.doi.org/10.17533/udea.le.n71a4820.

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El objetivo del trabajo es analizar el uso de un conjunto de estrategias para minimizar el gasto en telefonía móvil en una encuesta de telefonía móvil para personas de bajos ingresos en Argentina, Brasil, Colombia, México y Perú. La metodología empleada incluye dos etapas; primero, se evalúa cuáles son los determinantes del uso de cada estrategia mediante un modelo probabilístico y se encuentra que la edad y el nivel de escolaridad influyen positivamente en la probabilidad de usar las alternativas; segundo, se utiliza un modelo de Poisson para evaluar el número de estrategias utilizadas. Aunqu

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Yerokhin, A. L., and O. V. Zolotukhin. "Fuzzy probabilistic neural network in document classification tasks." Information extraction and processing 2018, no. 46 (2018): 68–71. http://dx.doi.org/10.15407/vidbir2018.46.068.

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Ousmane, Cissé. "Diagnostic spatial et Perception des agriculteurs sur la fragmentation de la Forêt Classée De Mbao (FCM), Dakar, Sénégal." Revue Ecosystèmes et Paysages 4, no. 2 (2024): 1–10. https://doi.org/10.59384/recopays.tg4214.

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Aux défis de la fragmentation des écosystèmes naturels et son corolaire de perte de la biodiversité ou des services écosystémiques affiliés. Cette étude vise à diagnostiquer et analyser la perception des populations sur la fragmentation de la Forêt Classée de Mbao (FCM), au Sénégal. Pour clarifier cette fragmentation paysagère, une étude paysagère a été menée à partir d'une image Landsat de 2024, acquise à partir du capteur OLI. Une classification supervisée a été effectuée, et sa fiabilité a été vérifiée. La perception paysagère a été mise en évidence et corrélée avec le calcul de l'indice de

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7

Selianinau, Mikhail. "Podejście probabilistyczne do klasyfikacji cyfrowych obrazów twarzy." Prace Naukowe Akademii im. Jana Długosza w Częstochowie. Technika, Informatyka, Inżynieria Bezpieczeństwa 6 (2018): 563–74. http://dx.doi.org/10.16926/tiib.2018.06.40.

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8

Subhankar, Manna, and G. Malathi. "PERFORMANCE ANALYSIS OF CLASSIFICATION ALGORITHM ON DIABETES HEALTHCARE DATASET." International Journal of Research - Granthaalayah 5, no. 8 (2017): 260–66. https://doi.org/10.5281/zenodo.890581.

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Healthcare industry collects huge amount of unclassified data every day. For an effective diagnosis and decision making, we need to discover hidden data patterns. An instance of such dataset is associated with a group of metabolic diseases that vary greatly in their range of attributes. The objective of this paper is to classify the diabetic dataset using classification techniques like Naive Bayes, ID3 and k means classification. The secondary objective is to study the performance of various classification algorithms used in this work. We propose to implement the classification algorithm using

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9

Gouiouez, Mounir. "Probabilistic Graphical Model based on BablNet for Arabic Text Classification." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (2020): 1241–50. http://dx.doi.org/10.5373/jardcs/v12sp7/20202224.

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10

Yang, Na, and Yongtao Zhang. "A Gaussian Process Classification and Target Recognition Algorithm for SAR Images." Scientific Programming 2022 (January 20, 2022): 1–10. http://dx.doi.org/10.1155/2022/9212856.

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Synthetic aperture Radar (SAR) uses the relative movement of the Radar and the target to pick up echoes of the detected area and image it. In contrast to optical imaging, SAR imaging systems are not affected by weather and time and can detect targets in harsh conditions. Therefore, the SAR image has important application value in military and civilian purposes. This paper introduces the classification of Gaussian process. Gaussian process classification is a probabilistic classification algorithm based on Bass frame. This is a complete probability expression. Based on Gaussian process and SAR

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11

Villa, Joe Luis, Ricard Boqué, and Joan Ferré. "Calculation of the probability of correct classification in probabilistic bagged k-Nearest Neighbours." Chemometrics and Intelligent Laboratory Systems 94, no. 1 (2008): 51–59. http://dx.doi.org/10.1016/j.chemolab.2008.06.007.

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12

Shi, Li Jun, Xian Cheng Mao, and Zheng Lin Peng. "Method for Classification of Remote Sensing Images Based on Multiple Classifiers Combination." Applied Mechanics and Materials 263-266 (December 2012): 2561–65. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2561.

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This paper presents a new method for classification of remote sensing image based on multiple classifiers combination. In this method, three supervised classifications such as Mahalanobis Distance, Maximum Likelihood and SVM are selected to sever as the sub-classifications. The simple vote classification, maximum probability category method and fuzzy integral method are combined together according to certain rules. And adopted color infrared aerial images of Huairen country as the experimental object. The results show that the overall classification accuracy was improved by 12% and Kappa coeff

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13

Kuc, Roman. "Binaural echo error detection gives self-supervised learning to improve landmark classification." Journal of the Acoustical Society of America 156, no. 4_Supplement (2024): A111. https://doi.org/10.1121/10.0035283.

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Binaural processing is typically associated with source localization using interaural time and level differences. This paper describes its role for improving landmark classification from an echo sequence. A brain-inspired system explores the blind human echolocation problem of differentiating two foliage targets with different sized leaves using audible echoes. A biomimetic sonar views each target by producing left and right-ear monaural echo waveforms whose target-specific frequency power spectra are classified using template matching. Binaural processing implements error detection by ignorin

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DASHEVSKIY, MIKHAIL, and ZHIYUAN LUO. "RELIABLE PROBABILISTIC CLASSIFICATION OF INTERNET TRAFFIC." International Journal of Information Acquisition 06, no. 02 (2009): 133–46. http://dx.doi.org/10.1142/s0219878909001837.

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Classification of Internet traffic is very important to many applications such as network resource management, network security enforcement and intrusion detection. Many machine-learning algorithms have been successfully used to classify network traffic flows with good performance, but without information about the reliability in classifications. In this paper, we present a recently developed algorithmic framework, namely the Venn Probability Machine, for making reliable decisions under uncertainty. Experiments on publicly available real Internet traffic datasets show the algorithmic framework

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15

Zhao, Yue, Ye Yuan, and Guoren Wang. "Keyword Search over Probabilistic XML Documents Based on Node Classification." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/210961.

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This paper describes a keyword search measure on probabilistic XML data based on ELM (extreme learning machine). We use this method to carry out keyword search on probabilistic XML data. A probabilistic XML document differs from a traditional XML document to realize keyword search in the consideration of possible world semantics. A probabilistic XML document can be seen as a set of nodes consisting of ordinary nodes and distributional nodes. ELM has good performance in text classification applications. As the typical semistructured data; the label of XML data possesses the function of definiti

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16

Ribeiro, Rodrigo Otávio de Araújo, Lidia Angulo Meza, and Annibal Parracho Sant'Anna. "Probabilistic Preferences Composition in the Classification of Apparel Retail Stores." International Journal of Business Analytics 2, no. 4 (2015): 64–78. http://dx.doi.org/10.4018/ijban.2015100104.

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This paper employs the probabilistic composition of preferences to classify stores by their operational efficiency. Probabilistic composition of preferences is a multicriteria analysis methodology based on the transformation of assessments by multiple attributes into probabilities of choice. The numerical initial measurements provide estimates for location parameters of probability distributions that are compared to measure the preferences. The probabilities of choice according to each attribute separately are aggregated according to probabilistic composition rules. A classification of two set

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17

Huanhuan Chen, P. Tino, and Xin Yao. "Probabilistic Classification Vector Machines." IEEE Transactions on Neural Networks 20, no. 6 (2009): 901–14. http://dx.doi.org/10.1109/tnn.2009.2014161.

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18

Fava, Bruno, Paulo C. Marques F., and Hedibert F. Lopes. "Probabilistic Nearest Neighbors Classification." Entropy 26, no. 1 (2023): 39. http://dx.doi.org/10.3390/e26010039.

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Analysis of the currently established Bayesian nearest neighbors classification model points to a connection between the computation of its normalizing constant and issues of NP-completeness. An alternative predictive model constructed by aggregating the predictive distributions of simpler nonlocal models is proposed, and analytic expressions for the normalizing constants of these nonlocal models are derived, ensuring polynomial time computation without approximations. Experiments with synthetic and real datasets showcase the predictive performance of the proposed predictive model.

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19

Haberman, Shelby J. "PROBABILITY PREDICTION AND CLASSIFICATION." ETS Research Report Series 2004, no. 1 (2004): i—23. http://dx.doi.org/10.1002/j.2333-8504.2004.tb01946.x.

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20

Pernin, Jérôme, Mathieu Vrac, Cyril Crevoisier, and Alain Chédin. "Mixture model-based atmospheric air mass classification: a probabilistic view of thermodynamic profiles." Advances in Statistical Climatology, Meteorology and Oceanography 2, no. 2 (2016): 115–36. http://dx.doi.org/10.5194/ascmo-2-115-2016.

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Abstract. Air mass classification has become an important area in synoptic climatology, simplifying the complexity of the atmosphere by dividing the atmosphere into discrete similar thermodynamic patterns. However, the constant growth of atmospheric databases in both size and complexity implies the need to develop new adaptive classifications. Here, we propose a robust unsupervised and supervised classification methodology of a large thermodynamic dataset, on a global scale and over several years, into discrete air mass groups homogeneous in both temperature and humidity that also provides und

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21

Wazarkar, Seema, Bettahally N. Keshavamurthy, and Ahsan Hussain. "Probabilistic Classifier for Fashion Image Grouping Using Multi-Layer Feature Extraction Model." International Journal of Web Services Research 15, no. 2 (2018): 89–104. http://dx.doi.org/10.4018/ijwsr.2018040105.

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In this article, probabilistic classification model is designed for the fashion-related images collected from social networks. The proposed model is divided into two parts. The first is feature extraction where six important features are taken into consideration to deal with heterogeneous nature of the given images. The second classification is done with the help of probability computations to get collection of homogeneous images. Here, class-conditional probability of extracted features are calculated, then joint probability is used for the classification. Class label with maximum joint proba

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22

Jia, Li, Hai Yan, Guo Hui Li, and Hui Zhang. "Target Classification Using PAS and Evidence Theory." Applied Mechanics and Materials 347-350 (August 2013): 3728–33. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3728.

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This paper presents a novel Dempster-Shafer evidence construction approach for aircraft aim recognition. The prior-probability of the properties of aircraft was used for establishing a probabilistic argumentation system. Dempster-Shafer evidence was constructed by assumption-based reasoning. Therefore, additional information could be provided to the classification of the data fusion system. Experiments on artificial and real data demonstrated that the proposed method could improve the classification results.

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23

Ke, Wen-Chyan. "The Sensitivity to Trade Classification Algorithms for Estimating the Probability of Informed Trading." International Journal of Trade, Economics and Finance 5, no. 5 (2014): 392–96. http://dx.doi.org/10.7763/ijtef.2014.v5.404.

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24

Khan, Khalil, Muhammad Attique, Ikram Syed, and Asma Gul. "Automatic Gender Classification through Face Segmentation." Symmetry 11, no. 6 (2019): 770. http://dx.doi.org/10.3390/sym11060770.

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Automatic gender classification is challenging due to large variations of face images, particularly in the un-constrained scenarios. In this paper, we propose a framework which first segments a face image into face parts, and then performs automatic gender classification. We trained a Conditional Random Fields (CRFs) based segmentation model through manually labeled face images. The CRFs based model is used to segment a face image into six different classes—mouth, hair, eyes, nose, skin, and back. The probabilistic classification strategy (PCS) is used, and probability maps are created for all

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25

Duarte-Mermoud, M. A., N. H. Beltrán, and S. A. Salah. "Probabilistic Adaptive Crossover Applied to Chilean Wine Classification." Mathematical Problems in Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/734151.

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Recently, a new crossover technique for genetic algorithms has been proposed. The technique, called probabilistic adaptive crossover (PAX), includes the estimation of the probability distribution of the population, storing the information regarding the best and the worst solutions of the problem being solved in a probability vector. The use of the proposed technique to face Chilean wine classification based on chromatograms obtained from an HPLC is reported in this paper. PAX is used in the first stage as the feature selection method and then support vector machines (SVM) and linear discrimina

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26

Jangirov, A., G. Umirova, A. Abdullina, and I. Karpenko. "Feasibility study of seismic AVO-inversion and seismic inversion capabilities in conditions of acoustically weak-contrast reservoirs and host rocks." Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, no. 3 (June 25, 2025): 21–28. https://doi.org/10.33271/nvngu/2025-3/021.

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Purpose. To improve the accuracy of lithotype classification and reduce uncertainty in hydrocarbon prospecting predictions under conditions of weak contrast in the elastic properties of reservoirs and the surrounding rock by testing a probabilistic approach to seismic data interpretation. Methodology. The study applies a technological approach that includes: 1. Constructing probability distribution functions for lithotypes based on lithological trend data from wells. 2. Using inversion to create cubes of elastic properties and their further analysis using Bayes’ principle. 3. Lithotype classif

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27

Lee, Youngjae, and Hyeyoung Park. "Effect of Probabilistic Similarity Measure on Metric-Based Few-Shot Classification." Applied Sciences 11, no. 22 (2021): 10977. http://dx.doi.org/10.3390/app112210977.

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In developing a few-shot classification model using deep networks, the limited number of samples in each class causes difficulty in utilizing statistical characteristics of the class distributions. In this paper, we propose a method to treat this difficulty by combining a probabilistic similarity based on intra-class statistics with a metric-based few-shot classification model. Noting that the probabilistic similarity estimated from intra-class statistics and the classifier of conventional few-shot classification models have a common assumption on the class distributions, we propose to apply t

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28

FERSINI, ELISABETTA, and ENZA MESSINA. "WEB PAGE CLASSIFICATION THROUGH PROBABILISTIC RELATIONAL MODELS." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 04 (2013): 1350013. http://dx.doi.org/10.1142/s0218001413500134.

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In the last decade, new approaches focused on modeling uncertainty over complex relational data have been developed. In this paper, one of the most promising of such approaches, known as probabilistic relational model (PRM), has been investigated and extended in order to measure and include semantic relationships for addressing web page classification problems. Experimental results show the potential of the proposed method of capturing the "strength" of existing relationships (links) and the capacity of including this information into the probability model.

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29

She, Qingshan, Yuliang Ma, Ming Meng, and Zhizeng Luo. "Multiclass Posterior Probability Twin SVM for Motor Imagery EEG Classification." Computational Intelligence and Neuroscience 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/251945.

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Motor imagery electroencephalography is widely used in the brain-computer interface systems. Due to inherent characteristics of electroencephalography signals, accurate and real-time multiclass classification is always challenging. In order to solve this problem, a multiclass posterior probability solution for twin SVM is proposed by the ranking continuous output and pairwise coupling in this paper. First, two-class posterior probability model is constructed to approximate the posterior probability by the ranking continuous output techniques and Platt’s estimating method. Secondly, a solution

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Amirgaliyev, Yedilkhan, Vladimir Berikov, Lyailya Cherikbayeva, Konstantin Latuta, and Kalybekuuly Bekturgan. "Group approach to solving the tasks of recognition." Yugoslav Journal of Operations Research 29, no. 2 (2019): 177–92. http://dx.doi.org/10.2298/yjor180822032y.

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In this work, we develop CASVM and CANN algorithms for semi-supervised classification problem. The algorithms are based on a combination of ensemble clustering and kernel methods. Probabilistic model of classification with use of cluster ensemble is proposed. Within the model, error probability of CANN is studied. Assumptions that make probability of error converge to zero are formulated. The proposed algorithms are experimentally tested on a hyperspectral image. It is shown that CASVM and CANN are more noise resistant than standard SVM and kNN.

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Ech-Chelfi, Wiame, and Hammoumi El. "Survey on the relation between road freight transport, SCM and sustainable development." Yugoslav Journal of Operations Research 29, no. 2 (2019): 151–76. http://dx.doi.org/10.2298/yjor180915006e.

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In this work, we develop CASVM and CANN algorithms for semi-supervised classification problem. The algorithms are based on a combination of ensemble clustering and kernel methods. A probabilistic model of classification with the use of cluster ensemble is proposed. Within the model, error probability of CANN is studied. Assumptions that make probability of error converge to zero are formulated. The proposed algorithms are experimentally tested on a hyperspectral image. It is shown that CASVM and CANN are more noise resistant than standard SVM and kNN.

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Andrianomena, Sambatra. "Probabilistic learning for pulsar classification." Journal of Cosmology and Astroparticle Physics 2022, no. 10 (2022): 016. http://dx.doi.org/10.1088/1475-7516/2022/10/016.

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Abstract In this work, we explore the possibility of using probabilistic learning to identify pulsar candidates. We make use of Deep Gaussian Process (DGP) and Deep Kernel Learning (DKL). Trained on a balanced training set in order to avoid the effect of class imbalance, the performance of the models, achieving relatively high probability of differentiating the positive class from the negative one (roc-auc ∼ 0.98), is very promising overall. We estimate the predictive entropy of each model predictions and find that DKL is more confident than DGP in its predictions and provides better uncertain

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Knowlton, B. J., L. R. Squire, and M. A. Gluck. "Probabilistic classification learning in amnesia." Learning & Memory 1, no. 2 (1994): 106–20. http://dx.doi.org/10.1101/lm.1.2.106.

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Amnesic patients and control subjects participated in a study of probabilistic classification learning. In each of three tasks, four different cues were each probabilistically associated with one of two outcomes. On each trial, the cues could appear alone or in combination with other cues and subjects selected the outcome they thought was correct. Feedback was provided after each trial. In each task, the amnesic patients learned gradually to associate the cues with the appropriate outcome at the same rate as control subjects, improving from 50% correct to approximately 65% correct. Presumably

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Di Mauro, Nicola, Claudio Taranto, and Floriana Esposito. "Link classification with probabilistic graphs." Journal of Intelligent Information Systems 42, no. 2 (2014): 181–206. http://dx.doi.org/10.1007/s10844-013-0293-0.

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Lyu, Shengfei, Xing Tian, Yang Li, Bingbing Jiang, and Huanhuan Chen. "Multiclass Probabilistic Classification Vector Machine." IEEE Transactions on Neural Networks and Learning Systems 31, no. 10 (2020): 3906–19. http://dx.doi.org/10.1109/tnnls.2019.2947309.

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Watson-Daniels, Jamelle, David C. Parkes, and Berk Ustun. "Predictive Multiplicity in Probabilistic Classification." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (2023): 10306–14. http://dx.doi.org/10.1609/aaai.v37i9.26227.

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Machine learning models are often used to inform real world risk assessment tasks: predicting consumer default risk, predicting whether a person suffers from a serious illness, or predicting a person's risk to appear in court. Given multiple models that perform almost equally well for a prediction task, to what extent do predictions vary across these models? If predictions are relatively consistent for similar models, then the standard approach of choosing the model that optimizes a penalized loss suffices. But what if predictions vary significantly for similar models? In machine learning, thi

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Lewicki, Michael S. "Bayesian Modeling and Classification of Neural Signals." Neural Computation 6, no. 5 (1994): 1005–30. http://dx.doi.org/10.1162/neco.1994.6.5.1005.

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Identifying and classifying action potential shapes in extracellular neural waveforms have long been the subject of research, and although several algorithms for this purpose have been successfully applied, their use has been limited by some outstanding problems. The first is how to determine shapes of the action potentials in the waveform and, second, how to decide how many shapes are distinct. A harder problem is that action potentials frequently overlap making difficult both the determination of the shapes and the classification of the spikes. In this report, a solution to each of these pro

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Andreev, O. A., and A. T. Trofimov. "SYNTHESIS OF NEURAL NETWORK ALGORITHMS FOR CLASSIFICATION OF MARINE OBJECTS IN LOW-FREQUENCY PASSIVE SONAR SYSTEMS." Bulletin of Dubna International University for Nature, Society, and Man. Series: Natural and engineering sciences, no. 3 (44) (December 27, 2019): 3–8. http://dx.doi.org/10.37005/1818-0744-2019-3-3-8.

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The paper addresses the issue of insuring the required probability of correct classification of marine objects in low-frequency passive sonar systems. The solution to the issue is sought through the application of methods for the synthesis of neural network classification algorithms using poly-Gaussian probabilistic models (Gaussian mixture models, GMM). It is shown that the use of GMM makes it possible to solve a number of problems specific to the issue; classification algorithms synthesized using mentioned methods can be implemented in the form of neural networks, which in turn can be descri

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Wagh, Trupti, Jolnar Assi, and Ammar H. Mohammed. "Probability Graphical Model for Predicting Probability of Default For Mortgage Loans." International Journal of Data Science and Advanced Analytics 5, no. 5 (2023): 244–50. http://dx.doi.org/10.69511/ijdsaa.v5i5.210.

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Assessment of Default risk of borrowers is important for lending institutions as it directly affects profits and losses of the firm and guides in compensating the risk by taking appropriate majors for loans having higher probability of default. Predicting probability of default using statistical and machine learning models has been a popular research topic in data science community. While different types of classification models have been proposed historically, there is scope to apply probabilistic inference to the mortgage default analysis to support decision making. Probabilistic Graphical M

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Hartigan, J. A. "Introduction: Classification, probability and statistics." Computational Statistics & Data Analysis 23, no. 1 (1996): 3–4. http://dx.doi.org/10.1016/s0167-9473(96)00017-5.

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Nikolić, Miroslav, Danilo Nikolić, Miroslav Stefanović, Sara Koprivica, and Darko Stefanović. "Mitigating Algorithmic Bias Through Probability Calibration: A Case Study on Lead Generation Data." Mathematics 13, no. 13 (2025): 2183. https://doi.org/10.3390/math13132183.

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Probability calibration is commonly utilized to enhance the reliability and interpretability of probabilistic classifiers, yet its potential for reducing algorithmic bias remains under-explored. In this study, the role of probability calibration techniques in mitigating bias associated with sensitive attributes, specifically country of origin, within binary classification models is investigated. Using a real-world lead-generation 2853 × 8 matrix dataset characterized by substantial class imbalance, with the positive class representing 1.4% of observations, several binary classification models

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Babkov, Yu V., E. E. Belova, and M. I. Potapov. "On the classification of motive power failures." Dependability 21, no. 4 (2021): 12–19. http://dx.doi.org/10.21683/1729-2646-2021-21-4-12-19.

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The Aim of the article is to develop a motive power failure classification to enable substantiated definition of dependability requirements for motive power as a part of a railway transportation system, as well as for organizing systematic measures to ensure a required level of its dependability over the life cycle. Methods. The terminology of interstate dependability-related standards was analysed and the two classifications used by OJSC “RZD” for estimating the dependability of technical systems and motive power were compared. The dependability of railway transportation systems is studied us

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张, 俊达. "Volume Data Classification Visualization Based on Probabilistic Classification Model." Computer Science and Application 09, no. 11 (2019): 1986–92. http://dx.doi.org/10.12677/csa.2019.911223.

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Heese, Raoul, Patricia Bickert, and Astrid Elisa Niederle. "Representation of binary classification trees with binary features by quantum circuits." Quantum 6 (March 30, 2022): 676. http://dx.doi.org/10.22331/q-2022-03-30-676.

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We propose a quantum representation of binary classification trees with binary features based on a probabilistic approach. By using the quantum computer as a processor for probability distributions, a probabilistic traversal of the decision tree can be realized via measurements of a quantum circuit. We describe how tree inductions and the prediction of class labels of query data can be integrated into this framework. An on-demand sampling method enables predictions with a constant number of classical memory slots, independent of the tree depth. We experimentally study our approach using both a

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Zheng, Wei, and Feng Qian. "Promptly assessing probability of barge–bridge collision damage of piers through probabilistic-based classification of machine learning." Journal of Civil Structural Health Monitoring 7, no. 1 (2017): 57–78. http://dx.doi.org/10.1007/s13349-017-0208-9.

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Liu, Zhege, Junxing Cao, Yujia Lu, Shuna Chen, and Jianli Liu. "A seismic facies classification method based on the convolutional neural network and the probabilistic framework for seismic attributes and spatial classification." Interpretation 7, no. 3 (2019): SE225—SE236. http://dx.doi.org/10.1190/int-2018-0238.1.

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In the early stage of oil and gas exploration, due to the lack of available drilling data, the automatic seismic facies classification technology mainly relies on the unsupervised clustering method combined with the seismic multiattribute. However, the clustering results are unstable and have no clear geologic significance. The supervised classification method based on manual interpretation can provide corresponding geologic significance, but there are still some problems such as the discrete classification results and low accuracy. To solve these problems, inspired by hyperspectral and spatia

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SIVALINGAM, DAYAN MANOHAR, NARENKUMAR PANDIAN, and JEZEKIEL BEN-ARIE. "MINIMAL CLASSIFICATION METHOD WITH ERROR-CORRECTING CODES FOR MULTICLASS RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 05 (2005): 663–80. http://dx.doi.org/10.1142/s0218001405004241.

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In this work, we develop an efficient technique to transform a multiclass recognition problem into a minimal binary classification problem using the Minimal Classification Method (MCM). The MCM requires only log 2 N classifications whereas the other methods require much more. For the classification, we use Support Vector Machine (SVM) based binary classifiers since they have superior generalization performance. Unlike the prevalent one-versus-one strategy (the bottom-up one-versus-one strategy is called tournament method) that separates only two classes at each classification, the binary class

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von Specht, Sebastian, and Fabrice Cotton. "A Link between Machine Learning and Optimization in Ground-Motion Model Development: Weighted Mixed-Effects Regression with Data-Driven Probabilistic Earthquake Classification." Bulletin of the Seismological Society of America 110, no. 6 (2020): 2777–800. http://dx.doi.org/10.1785/0120190133.

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ABSTRACT The steady increase of ground-motion data not only allows new possibilities but also comes with new challenges in the development of ground-motion models (GMMs). Data classification techniques (e.g., cluster analysis) do not only produce deterministic classifications but also probabilistic classifications (e.g., probabilities for each datum to belong to a given class or cluster). One challenge is the integration of such continuous classification in regressions for GMM development such as the widely used mixed-effects model. We address this issue by introducing an extension of the mixe

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WANDJI, Georges, and KOUOTOU Anicet Clément MIMCHE. "Le logiciel sage Saari à l'épicentre du système d'information des entreprises au Cameroun : une culture comptable perçue à travers le modèle de l'acceptation technologique." International Journal of Accounting, Finance, Auditing, Management and Economics 2, no. 5 (2021): 567–78. https://doi.org/10.5281/zenodo.5528050.

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L’objectif de ce papier était de vérifier si le profil de l’entité, le profil du préparateur de comptes et le profil perçu du logiciel de gestion sage Saari permettent chacun en ce qui le concerne, l’usage quotidien de l’outil de gestion sage Saari  dans les entreprises au Cameroun. Le problème se pose en ceci que sur trente entreprises observées sur le terrain en prélude de cette étude, nous avons constaté qu’elles toutes (100%) ont réellement installé le logiciel de gestion

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BALLO, Issa, Houdou Attikou DIALLO, and Laya Amadou GUINDO. "Intégration des pratiques d'audit interne dans la gestion des communes et du District de Bamako." International Journal of Accounting, Finance, Auditing, Management and Economics 3, no. 3-2 (2022): 382–95. https://doi.org/10.5281/zenodo.6582465.

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Ce papier vise à intégrer des pratiques d’audit interne dans la gestion des communes et du District de Bamako afin d’améliorer leur gestion publique. L’atteinte de l’objectif passe par la réponse à la question qui s’intitule « pourquoi l’audit interne doit-il être intégré dans la gestion des communes et du District de Bamako ? ». Il s’agit de choisir l’approche hypothético-déductive pour valider ou invalider les hypothèses. Nous avons opté pou

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