Zeitschriftenartikel zum Thema „Probability learning“
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SAEKI, Daisuke. „Probability learning in golden hamsters“. Japanese Journal of Animal Psychology 49, Nr. 1 (1999): 41–47. http://dx.doi.org/10.2502/janip.49.41.
Groth, Randall E., Jennifer A. Bergner und Jathan W. Austin. „Dimensions of Learning Probability Vocabulary“. Journal for Research in Mathematics Education 51, Nr. 1 (Januar 2020): 75–104. http://dx.doi.org/10.5951/jresematheduc.2019.0008.
Groth, Randall E., Jennifer A. Bergner und Jathan W. Austin. „Dimensions of Learning Probability Vocabulary“. Journal for Research in Mathematics Education 51, Nr. 1 (Januar 2020): 75–104. http://dx.doi.org/10.5951/jresematheduc.51.1.0075.
Rivas, Javier. „Probability matching and reinforcement learning“. Journal of Mathematical Economics 49, Nr. 1 (Januar 2013): 17–21. http://dx.doi.org/10.1016/j.jmateco.2012.09.004.
West, Bruce J. „Fractal Probability Measures of Learning“. Methods 24, Nr. 4 (August 2001): 395–402. http://dx.doi.org/10.1006/meth.2001.1208.
Malley, J. D., J. Kruppa, A. Dasgupta, K. G. Malley und A. Ziegler. „Probability Machines“. Methods of Information in Medicine 51, Nr. 01 (2012): 74–81. http://dx.doi.org/10.3414/me00-01-0052.
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.
HIRASAWA, Kotaro, Masaaki HARADA, Masanao OHBAYASHI, Juuichi MURATA und Jinglu HU. „Probability and Possibility Automaton Learning Network“. IEEJ Transactions on Industry Applications 118, Nr. 3 (1998): 291–99. http://dx.doi.org/10.1541/ieejias.118.291.
Groth, Randall E., Jaime Butler und Delmar Nelson. „Overcoming challenges in learning probability vocabulary“. Teaching Statistics 38, Nr. 3 (26.05.2016): 102–7. http://dx.doi.org/10.1111/test.12109.
Starzyk, J. A., und F. Wang. „Dynamic Probability Estimator for Machine Learning“. IEEE Transactions on Neural Networks 15, Nr. 2 (März 2004): 298–308. http://dx.doi.org/10.1109/tnn.2004.824254.
Kabata, Takashi, Takemasa Yokoyama, Yasuki Noguchi und Shinichi Kita. „Location Probability Learning Requires Focal Attention“. Perception 43, Nr. 4 (Januar 2014): 344–50. http://dx.doi.org/10.1068/p7589.
Kreitler, Shulamith, und Edward Zigler. „Motivational Determinants of Children's Probability Learning“. Journal of Genetic Psychology 151, Nr. 3 (September 1990): 301–16. http://dx.doi.org/10.1080/00221325.1990.9914619.
Bialek, William, Curtis G. Callan und Steven P. Strong. „Field Theories for Learning Probability Distributions“. Physical Review Letters 77, Nr. 23 (02.12.1996): 4693–97. http://dx.doi.org/10.1103/physrevlett.77.4693.
Husmeier, D. „Learning non-stationary conditional probability distributions“. Neural Networks 13, Nr. 3 (April 2000): 287–90. http://dx.doi.org/10.1016/s0893-6080(00)00018-6.
Lungu, O. V., T. W�chter, T. Liu, D. T. Willingham und J. Ashe. „Probability detection mechanisms and motor learning“. Experimental Brain Research 159, Nr. 2 (16.07.2004): 135–50. http://dx.doi.org/10.1007/s00221-004-1945-7.
Tanujaya, Benidiktus, Rully Charitas Indra Prahmana und 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.
Schumacher, Martin. „Probability estimation and machine learning-Editorial“. Biometrical Journal 56, Nr. 4 (Juli 2014): 531–33. http://dx.doi.org/10.1002/bimj.201400075.
Rahmi, F., P. D. Sampoerno und L. Ambarwati. „Probability learning trajectory: Students’ emerging relational understanding of probability through ratio“. Journal of Physics: Conference Series 1470 (Februar 2020): 012067. http://dx.doi.org/10.1088/1742-6596/1470/1/012067.
Shi-Ming Huang, Shi-Ming Huang, Yu-Ting Huang Shi-Ming Huang und Li-Kuan Wang Yu-Ting Huang. „Teaching Case – Predicting the Probability of Company Bankruptcy with CAATs“. International Journal of Computer Auditing 2, Nr. 1 (Dezember 2020): 005–22. http://dx.doi.org/10.53106/256299802020120201002.
Chung, Heewon, und Jinseok Lee. „Iterative Semi-Supervised Learning Using Softmax Probability“. Computers, Materials & Continua 72, Nr. 3 (2022): 5607–28. http://dx.doi.org/10.32604/cmc.2022.028154.
Rastogi (nee Khemchandani), Reshma, und Sambhav Jain. „Multi-label learning via minimax probability machine“. International Journal of Approximate Reasoning 145 (Juni 2022): 1–17. http://dx.doi.org/10.1016/j.ijar.2022.02.002.
White, Chris M., und Derek J. Koehler. „Missing information in multiple-cue probability learning“. Memory & Cognition 32, Nr. 6 (September 2004): 1007–18. http://dx.doi.org/10.3758/bf03196877.
Munro, D. J., O. K. Ersoy, M. R. Bell und J. S. Sadowsky. „Neural network learning of low-probability events“. IEEE Transactions on Aerospace and Electronic Systems 32, Nr. 3 (Juli 1996): 898–910. http://dx.doi.org/10.1109/7.532251.
White, Chris M., und Derek J. Koehler. „Choice strategies in multiple-cue probability learning.“ Journal of Experimental Psychology: Learning, Memory, and Cognition 33, Nr. 4 (2007): 757–68. http://dx.doi.org/10.1037/0278-7393.33.4.757.
Koehler, Derek J. „Probability judgment in three-category classification learning.“ Journal of Experimental Psychology: Learning, Memory, and Cognition 26, Nr. 1 (2000): 28–52. http://dx.doi.org/10.1037/0278-7393.26.1.28.
Braga-Neto, Ulisses M., und Edward R. Dougherty. „Machine Learning Requires Probability and Statistics [Perspectives]“. IEEE Signal Processing Magazine 37, Nr. 4 (Juli 2020): 118–22. http://dx.doi.org/10.1109/msp.2020.2985385.
Cano, Andrés, Manuel Gómez-Olmedo, Serafín Moral, Cora B. Pérez-Ariza und Antonio Salmerón. „Learning recursive probability trees from probabilistic potentials“. International Journal of Approximate Reasoning 53, Nr. 9 (Dezember 2012): 1367–87. http://dx.doi.org/10.1016/j.ijar.2012.06.026.
FIORI, SIMONE. „PROBABILITY DENSITY FUNCTION LEARNING BY UNSUPERVISED NEURONS“. International Journal of Neural Systems 11, Nr. 05 (Oktober 2001): 399–417. http://dx.doi.org/10.1142/s0129065701000898.
Yang, Hongkang. „A Mathematical Framework for Learning Probability Distributions“. Journal of Machine Learning 1, Nr. 4 (Juni 2022): 373–431. http://dx.doi.org/10.4208/jml.221202.
Storkel, Holly L. „Learning New Words“. Journal of Speech, Language, and Hearing Research 44, Nr. 6 (Dezember 2001): 1321–37. http://dx.doi.org/10.1044/1092-4388(2001/103).
Wijaya, Ariyadi, Elmaini Elmaini und Michiel Doorman. „A LEARNING TRAJECTORY FOR PROBABILITY: A CASE OF GAME-BASED LEARNING“. Journal on Mathematics Education 12, Nr. 1 (01.01.2021): 1–16. http://dx.doi.org/10.22342/jme.12.1.12836.1-16.
Gnanasagaran, Durga, und Abdul Halim Amat @ Kamaruddin. „The effectiveness of mobile learning in the teaching and learning of probability“. Jurnal Pendidikan Sains Dan Matematik Malaysia 9, Nr. 2 (06.12.2019): 9–15. http://dx.doi.org/10.37134/jpsmm.vol9.2.2.2019.
Don, Hilary J., A. Ross Otto, Astin C. Cornwall, Tyler Davis und Darrell A. Worthy. „Learning reward frequency over reward probability: A tale of two learning rules“. Cognition 193 (Dezember 2019): 104042. http://dx.doi.org/10.1016/j.cognition.2019.104042.
CHERNOFF, EGAN J., EFI PAPARISTODEMOU, DIONYSIA BAKOGIANNI und PETER PETOCZ. „RESEARCH ON LEARNING AND TEACHING PROBABILITY WITHIN STATISTICS“. STATISTICS EDUCATION RESEARCH JOURNAL 15, Nr. 2 (30.11.2016): 6–10. http://dx.doi.org/10.52041/serj.v15i2.600.
Kosiashvili, D. „Probability of poverty: PPI analysis by machine learning“. 101, Nr. 101 (30.12.2021): 141–47. http://dx.doi.org/10.26565/2311-2379-2021-101-14.
Kertész, Gábor. „Deep Metric Learning Using Negative Sampling Probability Annealing“. Sensors 22, Nr. 19 (06.10.2022): 7579. http://dx.doi.org/10.3390/s22197579.
González-Santander, Juan Luis. „A probability problem suitable for Problem-Based Learning“. Nereis. Interdisciplinary Ibero-American Journal of Methods, Modelling and Simulation., Nr. 13 (15.11.2021): 165–72. http://dx.doi.org/10.46583/nereis_2021.13.782.
Yeh, Wei-Chang, Edward Lin und Chia-Ling Huang. „Predicting Spread Probability of Learning-Effect Computer Virus“. Complexity 2021 (10.07.2021): 1–17. http://dx.doi.org/10.1155/2021/6672630.
Catrambone, Richard, und Keith J. Holyoak. „Learning subgoals and methods for solving probability problems“. Memory & Cognition 18, Nr. 6 (November 1990): 593–603. http://dx.doi.org/10.3758/bf03197102.
Kaizhu Huang, Haiqin Yang, Irwin King und M. R. Lyu. „Imbalanced learning with a biased minimax probability machine“. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 36, Nr. 4 (August 2006): 913–23. http://dx.doi.org/10.1109/tsmcb.2006.870610.
Jović, Srđan, Milica Miljković, Miljan Ivanović, Milena Šaranović und Milena Arsić. „Prostate Cancer Probability Prediction By Machine Learning Technique“. Cancer Investigation 35, Nr. 10 (26.11.2017): 647–51. http://dx.doi.org/10.1080/07357907.2017.1406496.
Movellan, Javier R., und James L. McClelland. „Learning Continuous Probability Distributions with Symmetric Diffusion Networks“. Cognitive Science 17, Nr. 4 (Oktober 1993): 463–96. http://dx.doi.org/10.1207/s15516709cog1704_1.
Meade, R., B. Backus und Q. Haijiang. „Cue probability learning by the human perceptual system“. Journal of Vision 9, Nr. 8 (23.03.2010): 42. http://dx.doi.org/10.1167/9.8.42.
Delgado, M. R., M. M. Miller, S. Inati und E. A. Phelps. „An fMRI study of reward-related probability learning“. NeuroImage 24, Nr. 3 (Februar 2005): 862–73. http://dx.doi.org/10.1016/j.neuroimage.2004.10.002.
Cozman, Fabio Gagliardi. „Learning imprecise probability models: Conceptual and practical challenges“. International Journal of Approximate Reasoning 55, Nr. 7 (Oktober 2014): 1594–96. http://dx.doi.org/10.1016/j.ijar.2014.04.016.
Gaál, Zsófia Anna, Roland Boha, Brigitta Tóth und Márk Molnár. „Aging effect in an emotional probability learning task“. International Journal of Psychophysiology 77, Nr. 3 (September 2010): 257–58. http://dx.doi.org/10.1016/j.ijpsycho.2010.06.079.
Balata, Alessandro, Michael Ludkovski, Aditya Maheshwari und Jan Palczewski. „Statistical learning for probability-constrained stochastic optimal control“. European Journal of Operational Research 290, Nr. 2 (April 2021): 640–56. http://dx.doi.org/10.1016/j.ejor.2020.08.041.
T. Henry de Frahan, Marc, Shashank Yellapantula, Ryan King, Marc S. Day und Ray W. Grout. „Deep learning for presumed probability density function models“. Combustion and Flame 208 (Oktober 2019): 436–50. http://dx.doi.org/10.1016/j.combustflame.2019.07.015.
Xue, Di, Jingmei Li, Tu Lv, Weifei Wu und Jiaxiang Wang. „Malware Classification Using Probability Scoring and Machine Learning“. IEEE Access 7 (2019): 91641–56. http://dx.doi.org/10.1109/access.2019.2927552.
Rojarath, Artitayapron, und Wararat Songpan. „Probability-Weighted Voting Ensemble Learning for Classification Model“. Journal of Advances in Information Technology 11, Nr. 4 (2020): 217–27. http://dx.doi.org/10.12720/jait.11.4.217-227.