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Статті в журналах з теми "Probability learning":
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.
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.
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.
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.
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.
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.
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, 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.
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.
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.
Дисертації з теми "Probability learning":
Gozenman, Filiz. "Interaction Of Probability Learning And Working Memory." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614535/index.pdf.
RYSZ, TERI. "METACOGNITION IN LEARNING ELEMENTARY PROBABILITY AND STATISTICS." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1099248340.
Bouchacourt, Diane. "Task-oriented learning of structured probability distributions." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:0665495b-afbb-483b-8bdf-cbc6ae5baeff.
Li, Chengtao Ph D. Massachusetts Institute of Technology. "Diversity-inducing probability measures for machine learning." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/121724.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 163-176).
Subset selection problems arise in machine learning within kernel approximation, experimental design, and numerous other applications. In such applications, one often seeks to select diverse subsets of items to represent the population. One way to select such diverse subsets is to sample according to Diversity-Inducing Probability Measures (DIPMs) that assign higher probabilities to more diverse subsets. DIPMs underlie several recent breakthroughs in mathematics and theoretical computer science, but their power has not yet been explored for machine learning. In this thesis, we investigate DIPMs, their mathematical properties, sampling algorithms, and applications. Perhaps the best known instance of a DIPM is a Determinantal Point Process (DPP). DPPs originally arose in quantum physics, and are known to have deep relations to linear algebra, combinatorics, and geometry. We explore applications of DPPs to kernel matrix approximation and kernel ridge regression.
In these applications, DPPs deliver strong approximation guarantees and obtain superior performance compared to existing methods. We further develop an MCMC sampling algorithm accelerated by Gauss-type quadratures for DPPs. The algorithm runs several orders of magnitude faster than the existing ones. DPPs lie in a larger class of DIPMs called Strongly Rayleigh (SR) Measures. Instances of SR measures display a strong negative dependence property known as negative association, and as such can be used to model subset diversity. We study mathematical properties of SR measures, and construct the first provably fast-mixing Markov chain that samples from general SR measures. As a special case, we consider an SR measure called Dual Volume Sampling (DVS), for which we present the first poly-time sampling algorithm.
While all considered distributions over subsets are unconstrained, those of interest in the real world usually come with constraints due to prior knowledge, resource limitations or personal preferences. Hence we investigate sampling from constrained versions of DIPMs. Specifically, we consider DIPMs with cardinality constraints and matroid base constraints and construct poly-time approximate sampling algorithms for them. Such sampling algorithms will enable practical uses of constrained DIPMs in real world.
by Chengtao Li.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Hunt, Gareth David. "Reinforcement Learning for Low Probability High Impact Risks." Thesis, Curtin University, 2019. http://hdl.handle.net/20.500.11937/77106.
Słowiński, Witold. "Autonomous learning of domain models from probability distribution clusters." Thesis, University of Aberdeen, 2014. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=211059.
Benson, Carol Trinko Jones Graham A. "Assessing students' thinking in modeling probability contexts." Normal, Ill. Illinois State University, 2000. http://wwwlib.umi.com/cr/ilstu/fullcit?p9986725.
Title from title page screen, viewed May 11, 2006. Dissertation Committee: Graham A. Jones (chair), Kenneth N. Berk, Patricia Klass, Cynthia W. Langrall, Edward S. Mooney. Includes bibliographical references (leaves 115-124) and abstract. Also available in print.
Rast, Jeanne D. "A Comparison of Learning Subjective and Traditional Probability in Middle Grades." Digital Archive @ GSU, 2005. http://digitalarchive.gsu.edu/msit_diss/4.
Lindsay, David George. "Machine learning techniques for probability forecasting and their practical evaluations." Thesis, Royal Holloway, University of London, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.445274.
Kornfeld, Sarah. "Predicting Default Probability in Credit Risk using Machine Learning Algorithms." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275656.
Denna uppsats har undersökt internt utvecklade modeller för att estimera sannolikheten för utebliven betalning (PD) inom kreditrisk. Samtidigt som nya regelverk sätter restriktioner på metoder för modellering av kreditrisk och i viss mån hämmar utvecklingen av riskmätning, utvecklas samtidigt mer avancerade metoder inom maskinlärning för riskmätning. Således har avvägningen mellan strängare regelverk av internt utvecklade modeller och framsteg i dataanalys undersökts genom jämförelse av modellprestanda för referens metoden logistisk regression för uppskattning av PD med maskininlärningsteknikerna beslutsträd, Random Forest, Gradient Boosting och artificiella neurala nätverk (ANN). Dataunderlaget kommer från SEB och består utav 45 variabler och 24 635 observationer. När maskininlärningsteknikerna blir mer komplexa för att gynna förbättrad prestanda är det ofta på bekostnad av modellens tolkbarhet. En undersökande analys gjordes därför med målet att mäta förklarningsvariablers betydelse i maskininlärningsteknikerna. Resultaten från den undersökande analysen kommer att jämföras med resultat från etablerade metoder som mäter variabelsignifikans. Resultatet av studien visar att den logistiska regressionen presterade bättre än maskininlärningsteknikerna baserat på prestandamåttet AUC som mätte 0.906. Resultatet from den undersökande analysen för förklarningsvariablers betydelse ökade tolkbarheten för maskininlärningsteknikerna. Resultatet blev även validerat med utkomsten av de etablerade metoderna för att mäta variabelsignifikans.
Книги з теми "Probability learning":
Batanero, Carmen, Egan J. Chernoff, Joachim Engel, Hollylynne S. Lee, and Ernesto Sánchez. Research on Teaching and Learning Probability. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31625-3.
DasGupta, Anirban. Probability for Statistics and Machine Learning. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9634-3.
Peck, Roxy. Statistics: Learning from data. Australia: Brooks/Cole, Cengage Learning, 2014.
Unpingco, José. Python for Probability, Statistics, and Machine Learning. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18545-9.
Unpingco, José. Python for Probability, Statistics, and Machine Learning. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30717-6.
Unpingco, José. Python for Probability, Statistics, and Machine Learning. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04648-3.
Powell, Warren B. Optimal learning. Hoboken, New Jersey: Wiley, 2012.
Vapnik, Vladimir Naumovich. The Nature of Statistical Learning Theory. New York, NY: Springer New York, 1995.
DasGupta, Anirban. Probability for statistics and machine learning: Fundamentals and advanced topics. New York: Springer, 2011.
Wan, Shibiao. Machine learning for protein subcellular localization prediction. Boston: De Gruyter, 2015.
Частини книг з теми "Probability learning":
Zeugmann, Thomas, Pascal Poupart, James Kennedy, Xin Jin, Jiawei Han, Lorenza Saitta, Michele Sebag, et al. "Posterior Probability." In Encyclopedia of Machine Learning, 780. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_648.
Zeugmann, Thomas, Pascal Poupart, James Kennedy, Xin Jin, Jiawei Han, Lorenza Saitta, Michele Sebag, et al. "Prior Probability." In Encyclopedia of Machine Learning, 782. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_658.
Kumar Singh, Bikesh, and G. R. Sinha. "Probability Theory." In Machine Learning in Healthcare, 23–33. New York: CRC Press, 2022. http://dx.doi.org/10.1201/9781003097808-2.
Unpingco, José. "Probability." In Python for Probability, Statistics, and Machine Learning, 35–100. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30717-6_2.
Unpingco, José. "Probability." In Python for Probability, Statistics, and Machine Learning, 39–121. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18545-9_2.
Unpingco, José. "Probability." In Python for Probability, Statistics, and Machine Learning, 47–134. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04648-3_2.
Faul, A. C. "Probability Theory." In A Concise Introduction to Machine Learning, 7–61. Boca Raton, Florida : CRC Press, [2019] | Series: Chapman & Hall/CRC machine learning & pattern recognition: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781351204750-2.
Ghatak, Abhijit. "Probability and Distributions." In Machine Learning with R, 31–56. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6808-9_2.
Forsyth, David. "Clustering Using Probability Models." In Applied Machine Learning, 183–202. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18114-7_9.
Webb, Geoffrey I. "Posterior Probability." In Encyclopedia of Machine Learning and Data Mining, 989–90. Boston, MA: Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_648.
Тези доповідей конференцій з теми "Probability learning":
Temlyakov, V. N. "Optimal estimators in learning theory." In Approximation and Probability. Warsaw: Institute of Mathematics Polish Academy of Sciences, 2006. http://dx.doi.org/10.4064/bc72-0-23.
Neville, Jennifer, David Jensen, Lisa Friedland, and Michael Hay. "Learning relational probability trees." In the ninth ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/956750.956830.
Arieli, Itai, Yakov Babichenko, and Manuel Mueller-Frank. "Naive Learning Through Probability Matching." In EC '19: ACM Conference on Economics and Computation. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3328526.3329601.
Ha, Ming-hu, Zhi-fang Feng, Er-ling Du, and Yun-chao Bai. "Further Discussion on Quasi-Probability." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.258542.
Burgos, María, María Del Mar López-Martín, and Nicolás Tizón-Escamilla. "ALGEBRAIC REASONING IN PROBABILITY TASKS." In 14th International Conference on Education and New Learning Technologies. IATED, 2022. http://dx.doi.org/10.21125/edulearn.2022.0777.
Eugênio, Robson, Carlos Monteiro, Liliane Carvalho, José Roberto Costa Jr., and Karen François. "MATHEMATICS TEACHERS LEARNING ABOUT PROBABILITY LITERACY." In 14th International Technology, Education and Development Conference. IATED, 2020. http://dx.doi.org/10.21125/inted.2020.0272.
Silva, Jorge, and Shrikanth Narayanan. "Minimum Probability of Error Signal Representation." In 2007 IEEE Workshop on Machine Learning for Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/mlsp.2007.4414331.
Wang, Jingyi, and Boyang Zhang. "Survival Probability Assessment using Machine Learning Algorithms." In 2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE). IEEE, 2022. http://dx.doi.org/10.1109/mlise57402.2022.00097.
Scanlon, Eileen, Tim O'Shea, Randall B. Smith, and Yibing Li. "Supporting the distributed synchronous learning of probability." In the 2nd international conference. Morristown, NJ, USA: Association for Computational Linguistics, 1997. http://dx.doi.org/10.3115/1599773.1599801.
Shanbhag, Annapurna Anant, Chinmai Shetty, Alaka Ananth, Anjali Shridhar Shetty, K. Kavanashree Nayak, and B. R. Rakshitha. "Heart Attack Probability Analysis Using Machine Learning." In 2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER). IEEE, 2021. http://dx.doi.org/10.1109/discover52564.2021.9663631.
Звіти організацій з теми "Probability learning":
Shute, Valerie J., and Lisa A. Gawlick-Grendell. An Experimental Approach to Teaching and Learning Probability: Stat Lady. Fort Belvoir, VA: Defense Technical Information Center, April 1996. http://dx.doi.org/10.21236/ada316969.
Ilyin, M. E. The distance learning course «Theory of probability, mathematical statistics and random functions». OFERNIO, December 2018. http://dx.doi.org/10.12731/ofernio.2018.23529.
Kriegel, Francesco. Learning description logic axioms from discrete probability distributions over description graphs (Extended Version). Technische Universität Dresden, 2018. http://dx.doi.org/10.25368/2022.247.
Kriegel, Francesco. Learning General Concept Inclusions in Probabilistic Description Logics. Technische Universität Dresden, 2015. http://dx.doi.org/10.25368/2022.220.
Gribok, Andrei V., Kevin P. Chen, and Qirui Wang. Machine-Learning Enabled Evaluation of Probability of Piping Degradation In Secondary Systems of Nuclear Power Plants. Office of Scientific and Technical Information (OSTI), May 2020. http://dx.doi.org/10.2172/1634815.
Robson, Jennifer. The Canada Learning Bond, financial capability and tax-filing: Results from an online survey of low and modest income parents. SEED Winnipeg/Carleton University Arthur Kroeger College of Public Affairs, March 2022. http://dx.doi.org/10.22215/clb20220301.
Moreno Pérez, Carlos, and Marco Minozzo. “Making Text Talk”: The Minutes of the Central Bank of Brazil and the Real Economy. Madrid: Banco de España, November 2022. http://dx.doi.org/10.53479/23646.
Clausen, Jay, Vuong Truong, Sophia Bragdon, Susan Frankenstein, Anna Wagner, Rosa Affleck, and Christopher Williams. Buried-object-detection improvements incorporating environmental phenomenology into signature physics. Engineer Research and Development Center (U.S.), September 2022. http://dx.doi.org/10.21079/11681/45625.
Yaroshchuk, Svitlana O., Nonna N. Shapovalova, Andrii M. Striuk, Olena H. Rybalchenko, Iryna O. Dotsenko, and Svitlana V. Bilashenko. Credit scoring model for microfinance organizations. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3683.
Tyshchenko, Yelyzaveta Yu, та Andrii M. Striuk. Актуальність розробки моделі адаптивного навчання. [б. в.], грудень 2018. http://dx.doi.org/10.31812/123456789/2889.