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Journal articles on the topic 'Probability learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 28 July 2024

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1

SAEKI, Daisuke. "Probability learning in golden hamsters." Japanese Journal of Animal Psychology 49, no. 1 (1999): 41–47. http://dx.doi.org/10.2502/janip.49.41.

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2

Groth, Randall E., Jennifer A. Bergner, and Jathan W. Austin. "Dimensions of Learning Probability Vocabulary." Journal for Research in Mathematics Education 51, no. 1 (January 2020): 75–104. http://dx.doi.org/10.5951/jresematheduc.2019.0008.

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Normative discourse about probability requires shared meanings for disciplinary vocabulary. Previous research indicates that students’ meanings for probability vocabulary often differ from those of mathematicians, creating a need to attend to developing students’ use of language. Current standards documents conflict in their recommendations about how this should occur. In the present study, we conducted microgenetic research to examine the vocabulary use of four students before, during, and after lessons from a cycle of design-based research attending to probability vocabulary. In characterizing students’ normative and nonnormative uses of language, we draw implications for the design of curriculum, standards, and further research. Specifically, we illustrate the importance of attending to incrementality, multidimensionality, polysemy, interrelatedness, and heterogeneity to foster students’ probability vocabulary development.
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3

Groth, Randall E., Jennifer A. Bergner, and Jathan W. Austin. "Dimensions of Learning Probability Vocabulary." Journal for Research in Mathematics Education 51, no. 1 (January 2020): 75–104. http://dx.doi.org/10.5951/jresematheduc.51.1.0075.

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Normative discourse about probability requires shared meanings for disciplinary vocabulary. Previous research indicates that students’ meanings for probability vocabulary often differ from those of mathematicians, creating a need to attend to developing students’ use of language. Current standards documents conflict in their recommendations about how this should occur. In the present study, we conducted microgenetic research to examine the vocabulary use of four students before, during, and after lessons from a cycle of design-based research attending to probability vocabulary. In characterizing students’ normative and nonnormative uses of language, we draw implications for the design of curriculum, standards, and further research. Specifically, we illustrate the importance of attending to incrementality, multidimensionality, polysemy, interrelatedness, and heterogeneity to foster students’ probability vocabulary development.
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4

Rivas, Javier. "Probability matching and reinforcement learning." Journal of Mathematical Economics 49, no. 1 (January 2013): 17–21. http://dx.doi.org/10.1016/j.jmateco.2012.09.004.

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5

West, Bruce J. "Fractal Probability Measures of Learning." Methods 24, no. 4 (August 2001): 395–402. http://dx.doi.org/10.1006/meth.2001.1208.

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6

Jiang, Xiaolei. "Conditional Probability in Machine Learning." Journal of Education and Educational Research 4, no. 2 (July 20, 2023): 31–33. http://dx.doi.org/10.54097/jeer.v4i2.10647.

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To help teaching of machine learning course, manipulation rules and application examples of conditional probabilities in machine learning are presented. The emphasis is to make a clear distinction between reasonable assumptions and logical deductions developed from assumptions and axioms. The formula for conditional probability of conditional probability is presented with examples in Bayesian coin tossing, Bayesian linear regression, and Gaussian processes for regression and classification. The signal + noise model is formulated in terms of a proposition and exemplified by linear-Gaussian models and linear dynamical systems.
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7

Malley, J. D., J. Kruppa, A. Dasgupta, K. G. Malley, and A. Ziegler. "Probability Machines." Methods of Information in Medicine 51, no. 01 (2012): 74–81. http://dx.doi.org/10.3414/me00-01-0052.

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SummaryBackground: Most machine learning approaches only provide a classification for binary responses. However, probabilities are required for risk estimation using individual patient characteristics. It has been shown recently that every statistical learning machine known to be consistent for a nonparametric regression problem is a probability machine that is provably consistent for this estimation problem.Objectives: The aim of this paper is to show how random forests and nearest neighbors can be used for consistent estimation of individual probabilities.Methods: Two random forest algorithms and two nearest neighbor algorithms are described in detail for estimation of individual probabilities. We discuss the consistency of random forests, nearest neighbors and other learning machines in detail. We conduct a simulation study to illustrate the validity of the methods. We exemplify the algorithms by analyzing two well-known data sets on the diagnosis of appendicitis and the diagnosis of diabetes in Pima Indians.Results: Simulations demonstrate the validity of the method. With the real data application, we show the accuracy and practicality of this approach. We provide sample code from R packages in which the probability estimation is already available. This means that all calculations can be performed using existing software.Conclusions: Random forest algorithms as well as nearest neighbor approaches are valid machine learning methods for estimating individual probabilities for binary responses. Freely available implementations are available in R and may be used for applications.
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8

Dawson, Michael R. W. "Probability Learning by Perceptrons and People." Comparative Cognition & Behavior Reviews 15 (2022): 1–188. http://dx.doi.org/10.3819/ccbr.2019.140011.

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9

HIRASAWA, Kotaro, Masaaki HARADA, Masanao OHBAYASHI, Juuichi MURATA, and Jinglu HU. "Probability and Possibility Automaton Learning Network." IEEJ Transactions on Industry Applications 118, no. 3 (1998): 291–99. http://dx.doi.org/10.1541/ieejias.118.291.

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10

Groth, Randall E., Jaime Butler, and Delmar Nelson. "Overcoming challenges in learning probability vocabulary." Teaching Statistics 38, no. 3 (May 26, 2016): 102–7. http://dx.doi.org/10.1111/test.12109.

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11

Starzyk, J. A., and F. Wang. "Dynamic Probability Estimator for Machine Learning." IEEE Transactions on Neural Networks 15, no. 2 (March 2004): 298–308. http://dx.doi.org/10.1109/tnn.2004.824254.

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12

Kabata, Takashi, Takemasa Yokoyama, Yasuki Noguchi, and Shinichi Kita. "Location Probability Learning Requires Focal Attention." Perception 43, no. 4 (January 2014): 344–50. http://dx.doi.org/10.1068/p7589.

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13

Kreitler, Shulamith, and Edward Zigler. "Motivational Determinants of Children's Probability Learning." Journal of Genetic Psychology 151, no. 3 (September 1990): 301–16. http://dx.doi.org/10.1080/00221325.1990.9914619.

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14

Bialek, William, Curtis G. Callan, and Steven P. Strong. "Field Theories for Learning Probability Distributions." Physical Review Letters 77, no. 23 (December 2, 1996): 4693–97. http://dx.doi.org/10.1103/physrevlett.77.4693.

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15

Husmeier, D. "Learning non-stationary conditional probability distributions." Neural Networks 13, no. 3 (April 2000): 287–90. http://dx.doi.org/10.1016/s0893-6080(00)00018-6.

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16

Lungu, O. V., T. W�chter, T. Liu, D. T. Willingham, and J. Ashe. "Probability detection mechanisms and motor learning." Experimental Brain Research 159, no. 2 (July 16, 2004): 135–50. http://dx.doi.org/10.1007/s00221-004-1945-7.

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17

Tanujaya, Benidiktus, Rully Charitas Indra Prahmana, and Jeinne Mumu. "Designing learning activities on conditional probability." Journal of Physics: Conference Series 1088 (September 2018): 012087. http://dx.doi.org/10.1088/1742-6596/1088/1/012087.

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18

Schumacher, Martin. "Probability estimation and machine learning-Editorial." Biometrical Journal 56, no. 4 (July 2014): 531–33. http://dx.doi.org/10.1002/bimj.201400075.

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19

Rahmi, F., P. D. Sampoerno, and L. Ambarwati. "Probability learning trajectory: Students’ emerging relational understanding of probability through ratio." Journal of Physics: Conference Series 1470 (February 2020): 012067. http://dx.doi.org/10.1088/1742-6596/1470/1/012067.

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20

Geetha, Dr V., Dr C. K. Gomathy, Mr Maganti Dhanush, and Mr Bugga Sri Krishna Shyam. "PROBABILITY IN DECISION MAKING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (November 1, 2023): 1–11. http://dx.doi.org/10.55041/ijsrem27038.

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Probability is a fundamental concept in artificial intelligence (AI) that plays a crucial role in modelling uncertainty, making predictions, and decision-making. It forms the basis for various AI techniques, such as Bayesian networks, machine learning algorithms, and reinforcement learning. This article aims to provide a comprehensive overview of how probability is used in AI and its significance in the field.Probability is defined as the chance of happening or occurrences of an event. Generally, the possibility of analyzing the occurrence of any event with respect to previous data is called probability. Keywords: Bayesian networks, machine learning, Probability, Decision Making
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21

Shi-Ming Huang, Shi-Ming Huang, Yu-Ting Huang Shi-Ming Huang, and Li-Kuan Wang Yu-Ting Huang. "Teaching Case – Predicting the Probability of Company Bankruptcy with CAATs." International Journal of Computer Auditing 2, no. 1 (December 2020): 005–22. http://dx.doi.org/10.53106/256299802020120201002.

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<p>The paper provides a machine-learning experimental process for a real-world corporate financial bankruptcy case: Chunghwa Picture Tubes, Ltd., in Taiwan in 2019. The teaching case addresses major topics in financial bankruptcy analytics to enable business students to learn how to analyze leveraged finance and distressed debt and to predict bankruptcy. It is a science, technology, engineering, and mathematics (STEM) teaching case with a project-based learning method. The learning goal of the teaching case is to inspire and encourage students through planned teaching activities. Students start by thinking through problems or situations and establishing a machine-learning project using computer-assisted audit technique (CAAT) software. After students conduct a self-directed project, the student can use the new knowledge to develop a new bankruptcy-case analysis.</p> <p>&nbsp;</p>
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22

Chung, Heewon, and Jinseok Lee. "Iterative Semi-Supervised Learning Using Softmax Probability." Computers, Materials & Continua 72, no. 3 (2022): 5607–28. http://dx.doi.org/10.32604/cmc.2022.028154.

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23

Rastogi (nee Khemchandani), Reshma, and Sambhav Jain. "Multi-label learning via minimax probability machine." International Journal of Approximate Reasoning 145 (June 2022): 1–17. http://dx.doi.org/10.1016/j.ijar.2022.02.002.

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24

White, Chris M., and Derek J. Koehler. "Missing information in multiple-cue probability learning." Memory & Cognition 32, no. 6 (September 2004): 1007–18. http://dx.doi.org/10.3758/bf03196877.

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25

Munro, D. J., O. K. Ersoy, M. R. Bell, and J. S. Sadowsky. "Neural network learning of low-probability events." IEEE Transactions on Aerospace and Electronic Systems 32, no. 3 (July 1996): 898–910. http://dx.doi.org/10.1109/7.532251.

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26

White, Chris M., and Derek J. Koehler. "Choice strategies in multiple-cue probability learning." Journal of Experimental Psychology: Learning, Memory, and Cognition 33, no. 4 (2007): 757–68. http://dx.doi.org/10.1037/0278-7393.33.4.757.

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27

Koehler, Derek J. "Probability judgment in three-category classification learning." Journal of Experimental Psychology: Learning, Memory, and Cognition 26, no. 1 (2000): 28–52. http://dx.doi.org/10.1037/0278-7393.26.1.28.

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28

Braga-Neto, Ulisses M., and Edward R. Dougherty. "Machine Learning Requires Probability and Statistics [Perspectives]." IEEE Signal Processing Magazine 37, no. 4 (July 2020): 118–22. http://dx.doi.org/10.1109/msp.2020.2985385.

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29

Cano, Andrés, Manuel Gómez-Olmedo, Serafín Moral, Cora B. Pérez-Ariza, and Antonio Salmerón. "Learning recursive probability trees from probabilistic potentials." International Journal of Approximate Reasoning 53, no. 9 (December 2012): 1367–87. http://dx.doi.org/10.1016/j.ijar.2012.06.026.

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30

FIORI, SIMONE. "PROBABILITY DENSITY FUNCTION LEARNING BY UNSUPERVISED NEURONS." International Journal of Neural Systems 11, no. 05 (October 2001): 399–417. http://dx.doi.org/10.1142/s0129065701000898.

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In a recent work, we introduced the concept of pseudo-polynomial adaptive activation function neuron (FAN) and presented an unsupervised information-theoretic learning theory for such structure. The learning model is based on entropy optimization and provides a way of learning probability distributions from incomplete data. The aim of the present paper is to illustrate some theoretical features of the FAN neuron, to extend its learning theory to asymmetrical density function approximation, and to provide an analytical and numerical comparison with other known density function estimation methods, with special emphasis to the universal approximation ability. The paper also provides a survey of PDF learning from incomplete data, as well as results of several experiments performed on real-world problems and signals.
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31

Yang, Hongkang. "A Mathematical Framework for Learning Probability Distributions." Journal of Machine Learning 1, no. 4 (June 2022): 373–431. http://dx.doi.org/10.4208/jml.221202.

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32

Storkel, Holly L. "Learning New Words." Journal of Speech, Language, and Hearing Research 44, no. 6 (December 2001): 1321–37. http://dx.doi.org/10.1044/1092-4388(2001/103).

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Though the influences of syntactic and semantic regularity on novel word learning are well documented, considerably less is known about the influence of phono-logical regularities on lexical acquisition. The influence of phonotactic probability, a measure of the likelihood of occurrence of a sound sequence, on novel word learning is investigated in this study. Thirty-four typically developing children (from ages 3 years 2 months to 6 years 3 months) participated in a multitrial word-learning task involving nonwords of varying phonotactic probability (common vs. rare) paired with unfamiliar object referents. Form and referent learning were tested following increasing numbers of exposures (1 vs. 4 vs. 7) and following a 1-week delay. Correct responses were analyzed to determine whether phonotactic probability affected rate of word learning, and incorrect responses were analyzed to examine whether phonotactic probability affected the formation of semantic representations, lexical representations, or the association between semantic and lexical representations. Results indicated that common sound sequences were learned more rapidly than rare sound sequences across form and referent learning. In addition, phonotactic probability appeared to nfluence the formation of semantic representations and the association between semantic and lexical representations. These results are integrated with previous findings and theoretical models of language acquisition.
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33

Wijaya, Ariyadi, Elmaini Elmaini, and Michiel Doorman. "A LEARNING TRAJECTORY FOR PROBABILITY: A CASE OF GAME-BASED LEARNING." Journal on Mathematics Education 12, no. 1 (January 1, 2021): 1–16. http://dx.doi.org/10.22342/jme.12.1.12836.1-16.

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This research is aimed to describe a learning trajectory for probability through game-based learning. The research employed design research consisting of three stages: preparing for the experiment, design experiment, and retrospective analysis. A hypothetical learning trajectory (HLT) using Sudoku and Snake-and-ladder games was developed by collecting data through documentation, interviews, and classroom observations. The HLT was implemented in the classroom to investigate students’ actual learning trajectory. The results of this research indicate that the games helped students understand the concept of probability. The learning trajectory for probability based on game-based learning is seen from the perspective of four levels of emergent modeling. In the first level – ‘situational level’ – Sudoku and Ladder-and-Snake games were played by students. The second level is the ‘referential level’ where the rules of the games were used as a starting point to learn the concept of probability. Communication during game playing stimulated students' knowledge about random events, sample spaces, sample points, and events. At the third level – ‘general level’ – students used tree and table diagrams to generalize possible outcomes of an experiment and develop an understanding of sample spaces and sample points. Lastly, at the ‘formal level’ students developed their informal knowledge into formal concepts of probabilities.
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Sari, Atika Defita, Didi Suryadi, and Dadan Dasari. "Learning obstacle of probability learning based on the probabilistic thinking level." Journal on Mathematics Education 15, no. 1 (November 4, 2023): 207–26. http://dx.doi.org/10.22342/jme.v15i1.pp207-226.

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This study aims to determine students learning obstacles in probability material based on their probabilistic thinking (PT) level using the theory of didactical situation (TDS) perspective. This is qualitative research with the case study method. The subject consisted of 23 grade 9 students in junior high school who had studied the material and had taken the test. The test results were used to classify students depending on their PT level using the framework developed by Graha A. Jones. Furthermore, interviews were performed with three representative students from each PT level. The interviews indicated that students in each PT level continue to face learning obstacles, which include instrumental ontogenic and epistemological obstacles. The lowest PT level demonstrated a more complex and comprehensive learning obstacle in all constructs of PT. From the TDS perspective, all students who are at various levels of PT have reached an action situation. Only 33% of the students at the subjective level and 83% of the students at the transition level reached the formulation situation. Still, students at that level needed help to reach the other two situations. Unlike the two previous levels, students at the level of quantitative informal thinking can achieve situations of validation and institutionalization.
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35

Erkinovna, Ergasheva Fatima, and Egamberdiyeva Mohinur Fakhriddin kizi. "OTHER METHODS OF TEACHING PROBABILITY THEORY AND COMBINATORICS." American Journal of Applied Sciences 6, no. 3 (March 1, 2024): 13–15. http://dx.doi.org/10.37547/tajas/volume06issue03-03.

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The traditional methods of teaching probability theory and combinatorics often fail to engage students and promote deep understanding. This study explores alternative teaching strategies that incorporate active learning, technology, and real-world applications to enhance student comprehension and interest in these mathematical fields. By comparing the effectiveness of these methods with conventional approaches, this research aims to provide educators with practical insights for improving mathematics education.
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36

Gnanasagaran, Durga, and Abdul Halim Amat @ Kamaruddin. "The effectiveness of mobile learning in the teaching and learning of probability." Jurnal Pendidikan Sains Dan Matematik Malaysia 9, no. 2 (December 6, 2019): 9–15. http://dx.doi.org/10.37134/jpsmm.vol9.2.2.2019.

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This study investigates the effectiveness of mobile learning in the teaching and learning of Probability. The context of mobile here is not just restrained to gadgets such as smartphones but also the fact that teaching and learning can occur beyond boundaries and anywhere according to the convenience and personal preference of the students. This study made use of the pretest – posttest quasi experimental design and the students chosen for the study were from a pre-university college located in the northern region of the country. A total of 92 students made up the sample of the study. There were 46 students each in the experimental and control groups respectively. Cluster random sampling was employed as the sampling method here. The instrument used to collect data with the aim of strengthening the outcome of the study was the achievement test. A quantitative approach was undertaken specifically to analyse the obtained data. The paired sample t-test and independent sample t-test were executed in the data analysis process. Initially, every student involved in the study regardless of the group they were in possessed equal strength in their understanding of the content being covered as indicated by the results of their pre-test. The paired sample t-test yielded p < 0.05 which meant that there was a significant difference between the mean score of the pre-test and post-test in the experimental group and control group respectively. The outcome of the independent sample t-test showed that there was a significant difference between the mean score of the experimental group and that of the control group in the post-test (p < 0.05). This indicated that the treatment via mobile learning had indeed played a role in the improved performance of students in Probability, hence proving the effectiveness of mobile learning in the teaching and learning of Probability.
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37

Don, Hilary J., A. Ross Otto, Astin C. Cornwall, Tyler Davis, and Darrell A. Worthy. "Learning reward frequency over reward probability: A tale of two learning rules." Cognition 193 (December 2019): 104042. http://dx.doi.org/10.1016/j.cognition.2019.104042.

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38

CHERNOFF, EGAN J., EFI PAPARISTODEMOU, DIONYSIA BAKOGIANNI, and PETER PETOCZ. "RESEARCH ON LEARNING AND TEACHING PROBABILITY WITHIN STATISTICS." STATISTICS EDUCATION RESEARCH JOURNAL 15, no. 2 (November 30, 2016): 6–10. http://dx.doi.org/10.52041/serj.v15i2.600.

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39

Kosiashvili, D. "Probability of poverty: PPI analysis by machine learning." 101, no. 101 (December 30, 2021): 141–47. http://dx.doi.org/10.26565/2311-2379-2021-101-14.

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Recently, poverty has been recognized as a global problem. Poverty Probability Index (PPI) is one of the tools to measure it. Based on the survey results on household characteristics and asset ownership, the PPI calculates the likelihood that a household lives below the poverty line. PPI is currently used by more than 400 organizations around the world – international NGOs, social services, donors, investors, multinational corporations, government and other organizations in various sectors including agriculture, health, education, energy and finance. The most famous PPI-based projects include the “Hunger” and “Electronic Warehouse” projects, Starbucks' strategy for Colombian farmers. However, the basic model with two classes (poor-rich), which underlies the index, does not classify the majority of the population with an average level of income, which has a chance of both getting rich and falling into the poor class over time and under the influence of various exogenous factors. Therefore, the work suggests a clustering model, which allows to identify 3 categories of the population: in addition to the poor and the rich, it also considers people with average earnings. 1) The class of the poor includes people of middle and old age living in villages. In most cases, these are married women with low literacy rates, who do not have their own business, bank account, and often a telephone. 2) An average earner is often a young married man with a good education. In most cases, he is neither an investor nor a business owner, he does not have a home to rent. At the same time, he usually owns at least 2 phones. 3) The class of the rich includes people of both sexes, both single and with a family. These are highly educated people who most likely have a business, investments, apartments for rent. The proposed model will help to develop more accurate tools for both poverty alleviation and prevention.
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40

Kertész, Gábor. "Deep Metric Learning Using Negative Sampling Probability Annealing." Sensors 22, no. 19 (October 6, 2022): 7579. http://dx.doi.org/10.3390/s22197579.

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Multiple studies have concluded that the selection of input samples is key for deep metric learning. For triplet networks, the selection of the anchor, positive, and negative pairs is referred to as triplet mining. The selection of the negatives is considered the be the most complicated task, due to a large number of possibilities. The goal is to select a negative that results in a positive triplet loss; however, there are multiple approaches for this—semi-hard negative mining or hardest mining are well-known in addition to random selection. Since its introduction, semi-hard mining was proven to outperform other negative mining techniques; however, in recent years, the selection of the so-called hardest negative has shown promising results in different experiments. This paper introduces a novel negative sampling solution based on dynamic policy switching, referred to as negative sampling probability annealing, which aims to exploit the positives of all approaches. Results are validated on an experimental synthetic dataset using cluster-analysis methods; finally, the discriminative abilities of trained models are measured on real-life data.
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41

González-Santander, Juan Luis. "A probability problem suitable for Problem-Based Learning." Nereis. Interdisciplinary Ibero-American Journal of Methods, Modelling and Simulation., no. 13 (November 15, 2021): 165–72. http://dx.doi.org/10.46583/nereis_2021.13.782.

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We propose a simple probability problem for undergraduate level. This problem involves different branches of Mathematics, such as Graph Theory, Linear Algebra or hypergeometric sums, hence it is quite suitable to be used as Problem-Based Learning. In addition, the problem allows several variations so that it may be proposed to different groups of students at the same time.
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42

Yeh, Wei-Chang, Edward Lin, and Chia-Ling Huang. "Predicting Spread Probability of Learning-Effect Computer Virus." Complexity 2021 (July 10, 2021): 1–17. http://dx.doi.org/10.1155/2021/6672630.

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With the rapid development of network technology, computer viruses have developed at a fast pace. The threat of computer viruses persists because of the constant demand for computers and networks. When a computer virus infects a facility, the virus seeks to invade other facilities in the network by exploiting the convenience of the network protocol and the high connectivity of the network. Hence, there is an increasing need for accurate calculation of the probability of computer-virus-infected areas for developing corresponding strategies, for example, based on the possible virus-infected areas, to interrupt the relevant connections between the uninfected and infected computers in time. The spread of the computer virus forms a scale-free network whose node degree follows the power rule. A novel algorithm based on the binary-addition tree algorithm (BAT) is proposed to effectively predict the spread of computer viruses. The proposed BAT utilizes the probability derived from PageRank from the scale-free network together with the consideration of state vectors with both the temporal and learning effects. The performance of the proposed algorithm was verified via numerous experiments.
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43

Catrambone, Richard, and Keith J. Holyoak. "Learning subgoals and methods for solving probability problems." Memory & Cognition 18, no. 6 (November 1990): 593–603. http://dx.doi.org/10.3758/bf03197102.

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44

Kaizhu Huang, Haiqin Yang, Irwin King, and M. R. Lyu. "Imbalanced learning with a biased minimax probability machine." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 36, no. 4 (August 2006): 913–23. http://dx.doi.org/10.1109/tsmcb.2006.870610.

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45

Jović, Srđan, Milica Miljković, Miljan Ivanović, Milena Šaranović, and Milena Arsić. "Prostate Cancer Probability Prediction By Machine Learning Technique." Cancer Investigation 35, no. 10 (November 26, 2017): 647–51. http://dx.doi.org/10.1080/07357907.2017.1406496.

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46

Movellan, Javier R., and James L. McClelland. "Learning Continuous Probability Distributions with Symmetric Diffusion Networks." Cognitive Science 17, no. 4 (October 1993): 463–96. http://dx.doi.org/10.1207/s15516709cog1704_1.

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47

Meade, R., B. Backus, and Q. Haijiang. "Cue probability learning by the human perceptual system." Journal of Vision 9, no. 8 (March 23, 2010): 42. http://dx.doi.org/10.1167/9.8.42.

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48

Delgado, M. R., M. M. Miller, S. Inati, and E. A. Phelps. "An fMRI study of reward-related probability learning." NeuroImage 24, no. 3 (February 2005): 862–73. http://dx.doi.org/10.1016/j.neuroimage.2004.10.002.

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49

Cozman, Fabio Gagliardi. "Learning imprecise probability models: Conceptual and practical challenges." International Journal of Approximate Reasoning 55, no. 7 (October 2014): 1594–96. http://dx.doi.org/10.1016/j.ijar.2014.04.016.

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50

Gaál, Zsófia Anna, Roland Boha, Brigitta Tóth, and Márk Molnár. "Aging effect in an emotional probability learning task." International Journal of Psychophysiology 77, no. 3 (September 2010): 257–58. http://dx.doi.org/10.1016/j.ijpsycho.2010.06.079.

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