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Добірка наукової літератури з теми "Dynamics of learning"

Автор: Grafiati

Опубліковано: 23 вересня 2022

Оновлено: 28 вересня 2022

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Статті в журналах з теми "Dynamics of learning"

1

Kovacs, Alexander, Johann Fischbacher, Harald Oezelt, Markus Gusenbauer, Lukas Exl, Florian Bruckner, Dieter Suess, and Thomas Schrefl. "Learning magnetization dynamics." Journal of Magnetism and Magnetic Materials 491 (December 2019): 165548. http://dx.doi.org/10.1016/j.jmmm.2019.165548.

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2

Bergin, James, and Dan Bernhardt. "Comparative Learning Dynamics*." International Economic Review 45, no. 2 (May 2004): 431–65. http://dx.doi.org/10.1111/j.1468-2354.2004.00132.x.

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3

Gilbert, Charles D. "Learning: Neuronal dynamics and perceptual learning." Current Biology 4, no. 7 (July 1994): 627–29. http://dx.doi.org/10.1016/s0960-9822(00)00138-x.

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4

TRIFANESCU, Letitia. "Learning Dynamics in Feminine Precarious Migration. A Qualitative Perspective." Revista Romaneasca pentru Educatie Multidimensionala 5, no. 2 (December 31, 2013): 85–100. http://dx.doi.org/10.18662/rrem/2013.0502.08.

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5

Hou, Huaidian, and Lingxiao Wang. "Measuring Dynamics in Evacuation Behaviour with Deep Learning." Entropy 24, no. 2 (January 27, 2022): 198. http://dx.doi.org/10.3390/e24020198.

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Bounded rationality is one crucial component in human behaviours. It plays a key role in the typical collective behaviour of evacuation, in which heterogeneous information can lead to deviations from optimal choices. In this study, we propose a framework of deep learning to extract a key dynamical parameter that drives crowd evacuation behaviour in a cellular automaton (CA) model. On simulation data sets of a replica dynamic CA model, trained deep convolution neural networks (CNNs) can accurately predict dynamics from multiple frames of images. The dynamical parameter could be regarded as a factor describing the optimality of path-choosing decisions in evacuation behaviour. In addition, it should be noted that the performance of this method is robust to incomplete images, in which the information loss caused by cutting images does not hinder the feasibility of the method. Moreover, this framework provides us with a platform to quantitatively measure the optimal strategy in evacuation, and this approach can be extended to other well-designed crowd behaviour experiments.

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6

Fang, Aili, Kehua Yuan, Jinhua Geng, and Xinjiang Wei. "Opinion Dynamics with Bayesian Learning." Complexity 2020 (February 22, 2020): 1–5. http://dx.doi.org/10.1155/2020/8261392.

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Bayesian learning is a rational and effective strategy in the opinion dynamic process. In this paper, we theoretically prove that individual Bayesian learning can realize asymptotic learning and we test it by simulations on the Zachary network. Then, we propose a Bayesian social learning model with signal update strategy and apply the model on the Zachary network to observe opinion dynamics. Finally, we contrast the two learning strategies and find that Bayesian social learning can lead to asymptotic learning more faster than individual Bayesian learning.

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7

Rupčić, Nataša. "Learning-forgetting-unlearning-relearning – the learning organization’s learning dynamics." Learning Organization 26, no. 5 (July 8, 2019): 542–48. http://dx.doi.org/10.1108/tlo-07-2019-237.

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8

JOHANNES, MICHAEL, LARS A. LOCHSTOER, and YIQUN MOU. "Learning about Consumption Dynamics." Journal of Finance 71, no. 2 (March 18, 2016): 551–600. http://dx.doi.org/10.1111/jofi.12246.

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9

Häse, Florian, Stéphanie Valleau, Edward Pyzer-Knapp, and Alán Aspuru-Guzik. "Machine learning exciton dynamics." Chemical Science 7, no. 8 (2016): 5139–47. http://dx.doi.org/10.1039/c5sc04786b.

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10

Farber, H. S., and R. Gibbons. "Learning and Wage Dynamics." Quarterly Journal of Economics 111, no. 4 (November 1, 1996): 1007–47. http://dx.doi.org/10.2307/2946706.

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Дисертації з теми "Dynamics of learning"

1

Kapmeier, Florian. "Dynamics of interorganizational learning in learning alliances /." Frankfurt am Main [u.a.] : Lang, 2007. http://www.gbv.de/dms/zbw/525116672.pdf.

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2

Kulich, Martin. "Dynamic Template Adjustment in Continuous Keystroke Dynamics." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234927.

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Dynamika úhozů kláves je jednou z behaviorálních biometrických charakteristik, kterou je možné použít pro průběžnou autentizaci uživatelů. Vzhledem k tomu, že styl psaní na klávesnici se v čase mění, je potřeba rovněž upravovat biometrickou šablonu. Tímto problémem se dosud, alespoň pokud je autorovi známo, žádná studie nezabývala. Tato diplomová práce se pokouší tuto mezeru zaplnit. S pomocí dat o časování úhozů od 22 dobrovolníků bylo otestováno několik technik klasifikace, zda je možné je upravit na online klasifikátory, zdokonalující se bez učitele. Výrazné zlepšení v rozpoznání útočníka bylo zaznamenáno u jednotřídového statistického klasifikátoru založeného na normované Euklidovské vzdálenosti, v průměru o 23,7 % proti původní verzi bez adaptace, zlepšení však bylo pozorováno u všech testovacích sad. Změna míry rozpoznání správného uživatele se oproti tomu různila, avšak stále zůstávala na přijatelných hodnotách.

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3

Paenke, Ingo. "Dynamics of evolution and learning." Karlsruhe Univ.-Verl. Karlsruhe, 2008. http://d-nb.info/989361233/04.

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4

Giannitsarou, Chryssi. "Macroeconomic dynamics and adaptive learning." Thesis, London Business School (University of London), 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399325.

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5

Ribeiro, Andre Figueiredo. "Graph dynamics : learning and representation." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/34184.

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Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006.
Includes bibliographical references (p. 58-60).
Graphs are often used in artificial intelligence as means for symbolic knowledge representation. A graph is nothing more than a collection of symbols connected to each other in some fashion. For example, in computer vision a graph with five nodes and some edges can represent a table - where nodes correspond to particular shape descriptors for legs and a top, and edges to particular spatial relations. As a framework for representation, graphs invite us to simplify and view the world as objects of pure structure whose properties are fixed in time, while the phenomena they are supposed to model are actually often changing. A node alone cannot represent a table leg, for example, because a table leg is not one structure (it can have many different shapes, colors, or it can be seen in many different settings, lighting conditions, etc.) Theories of knowledge representation have in general concentrated on the stability of symbols - on the fact that people often use properties that remain unchanged across different contexts to represent an object (in vision, these properties are called invariants). However, on closer inspection, objects are variable as well as stable. How are we to understand such problems? How is that assembling a large collection of changing components into a system results in something that is an altogether stable collection of parts?
(cont.) The work here presents one approach that we came to encompass by the phrase "graph dynamics". Roughly speaking, dynamical systems are systems with states that evolve over time according to some lawful "motion". In graph dynamics, states are graphical structures, corresponding to different hypothesis for representation, and motion is the correction or repair of an antecedent structure. The adapted structure is an end product on a path of test and repair. In this way, a graph is not an exact record of the environment but a malleable construct that is gradually tightened to fit the form it is to reproduce. In particular, we explore the concept of attractors for the graph dynamical system. In dynamical systems theory, attractor states are states into which the system settles with the passage of time, and in graph dynamics they correspond to graphical states with many repairs (states that can cope with many different contingencies). In parallel with introducing the basic mathematical framework for graph dynamics, we define a game for its control, its attractor states and a method to find the attractors. From these insights, we work out two new algorithms, one for Bayesian network discovery and one for active learning, which in combination we use to undertake the object recognition problem in computer vision. To conclude, we report competitive results in standard and custom-made object recognition datasets.
by Andre Figueiredo Ribeiro.
S.M.

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6

Malfait, Nicole. "Characteristics of dynamics learning and generalization." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=85577.

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In order to grasp an object, the human nervous system must transform the intended hand displacement into control signals distributed to motor neurons and ultimately to muscles. The aim of this thesis is to describe the nature of the internal representations that the human motor system uses to perform reaching movements.
The aim of the first study was to provide a clear and simple way to test whether dynamical information is coded by the nervous system in an extrinsic, Cartesian, versus intrinsic, muscle- or joint-based, system of coordinates. As a means to determine the frame for reference used by the motor system, we examined how adaptation to externally applied forces transfers across different arm configurations. We trained subjects to make reaching movements while holding a robotic arm that applied forces proportional and perpendicular to the tangential velocity of the hand. While in the first trials hand paths were substantially deviated, subjects rapidly adapted to the new dynamic condition; they learned to compensate for the forces in order to restore the kinematics observed in the absence of load. Learning of the new dynamics transferred across movements performed in different regions of the workspace when the relation between joint displacements and experienced torques remained unchanged, rather than when the mapping between hand displacements and forces was preserved. This provided support to the idea that dynamics are encoded in muscle- or joint-based coordinates.
The results of the first study described a process of generalization that relies on the invariance of the mapping between torques and joint displacements. While this clearly points to an intrinsic coding of dynamics, it does not explain whether or how generalization over the workspace occurs when the pattern of torques changes with the configuration of the arm. In the second study, subjects learned a force field in which the forces acted always in the same direction relative to an external frame of reference, which defines a mapping between joint displacements and torques that varies with the configuration of the arm. Our idea was to test if in the absence of invariance in the pattern of torques, generalization would occur on the basis of the invariance in the direction of the forces represented in an extrinsic system of coordinates. (Abstract shortened by UMI.)

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7

Kim, Young Se. "Expectations, learning, and exchange rate dynamics." Connect to this title online, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1087229892.

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Анотація:

Thesis (Ph. D.)--Ohio State University, 2004.
Title from first page of PDF file. Document formatted into pages; contains xiii, 121 p.; also includes graphics (some col.) Includes bibliographical references (p. 117-121). Available online via OhioLINK's ETD Center

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8

Pradelski, Bary S. R. "Distributed dynamics and learning in games." Thesis, University of Oxford, 2015. http://ora.ox.ac.uk/objects/uuid:37185594-633c-4d78-a408-dfe4978bacb7.

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In this thesis we study decentralized dynamics for non-cooperative and cooperative games. The dynamics are behaviorally motivated and assume that very little information is available about other players' preferences, actions, or payoffs. For example, this is the case in markets where exchanges are frequent and the sheer size of the market hinders participants from learning about others' preferences. We consider learning dynamics that are based on trial-and-error and aspiration-based heuristics. Players occasionally try to increase their performance given their current payoffs. If successful they stick to the new action, otherwise they revert to their old action. We also study a dynamic model of social influence based on findings in sociology and psychology that people have a propensity to conform to others' behavior irrespective of the payoff consequences. We analyze the dynamics with a particular focus on two questions: How long does it take to reach equilibrium and what are the stability and welfare properties of the equilibria that the process selects? These questions are at the core of understanding which equilibrium concepts are robust in environments where players have little information about the game and the high rationality assumptions of standard game theory are not very realistic. Methodologically, this thesis builds on game theoretic techniques and prominent solution concepts such as the Nash equilibrium for non-cooperative games and the core for cooperative games, as well as refinement concepts like stochastic stability. The proofs rely on mathematical techniques from random walk theory and integer programming.

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9

Zhan, Beibei. "Learning crowd dynamics using computer vision." Thesis, Kingston University, 2008. http://eprints.kingston.ac.uk/20302/.

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An increase of violence in public spaces has prompted the introduction of more sophisticated technology to improve the safety and security of very crowded environments. Research disciplines such as civil engineering and sociology, have studied the crowd phenomenon for years, employing human visual observation to estimate the characteristics of a crowd. Computer vision researchers have increasingly been involved in the study and development of research methods for the automatic analysis of the crowd phenomenon. Until recently, most existing methods in computer vision have been focussed on extracting a limited number of features in controlled environments, with limited clutter and numbers of people. The main goal of this thesis is to advance the state of the art in computer vision methods for use in very crowded and cluttered environments. One of the aims is to devise a method that in the near future would be of help in other disciplines such as socio-dynamics and computer animation, where models of crowded scenes are built manually on painstaking visual observation. A series of novel methods is presented here that can learn crowd dynamics automatically by extracting different crowd information from real world crowded scenes and modelling crowd dynamics using computer vision. The developed methods include an individual behaviour classifier, a scene cluttering level estimator, two people counting schemes based on colour modelling and tracking, two algorithm for measuring crowd motion by matching local descriptors, and two dynamics modelling methods - one based on statistical techniques and the other one based on a neural network. The proposed information extracting methods are able to gather both macro information, which represents the properties of the whole crowd, and micro information, which is different from individual (location) to individual (location). The statistically-based dynamics modelling models the scene implicitly. Furthermore, a method for discovering the main path of the crowded scene is developed based on it. Self-Organizing Map (SOM) is chosen in the neural network approach of modelling dynamics; the resulting SOMs are proven to be able to capture the main dynamics of the crowded scene.

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10

Shah, Jagesh V. (Jagesh Vijaykumar). "Learning dynamics in feedforward neural networks." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/36541.

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Анотація:

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.
Includes bibliographical references (leaves 108-115).
by Jagesh V. Shah.
M.S.

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Книги з теми "Dynamics of learning"

1

Katcher, Allan. Learning dynamics. [Bloomington, Ind.?]: Xlibris, 2009.

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2

Farber, Henry S. Learning and wage dynamics. Cambridge, MA: National Bureau of Economic Research, 1991.

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3

Farber, Henry S. Learning and wage dynamics. Princeton: Princeton University, Industrial Relations Section, 1994.

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4

Motivational dynamics in language learning. Bristol: Multilingual Matters, 2015.

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5

Gourinchas, Pierre-Olivier. Exchange rate dynamics and learning. Cambridge, MA: National Bureau of Economic Research, 1996.

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6

Paenke, Ingo. Dynamics of evolution and learning. Karlsruhe: Universita tsverlag, 2008.

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7

Dörnyei, Zoltán, Peter D. MacIntyre, and Alastair Henry, eds. Motivational Dynamics in Language Learning. Bristol, Blue Ridge Summit: Multilingual Matters, 2014. http://dx.doi.org/10.21832/9781783092574.

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8

Roberts, Mark A. Exchange rate dynamics: Bubbles vs. learning. Reading: University of Reading Department of Economics, 1987.

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9

Gourinchas, Pierre-Olivier. Exchange rate dynamics, learning and misperception. Cambridge, Mass: National Bureau of Economic Research, 2002.

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10

Roberts, Mark A. Exchange rate dynamics: Bubbles vs. learning. Reading: University of Reading, 1987.

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Частини книг з теми "Dynamics of learning"

1

Atmanspacher, H. "Incommensurability of liouvillean dynamics and information dynamics." In Parallelism, Learning, Evolution, 482–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/3-540-55027-5_28.

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2

Kinzel, Wolfgang, and Manfred Opper. "Dynamics of Learning." In Models of Neural Networks, 149–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-97171-6_4.

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3

Kinzel, Wolfgang, and Manfred Opper. "Dynamics of Learning." In Models of Neural Networks I, 157–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-79814-6_4.

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4

Davison, Brian D. "Learning Web Request Patterns." In Web Dynamics, 435–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-10874-1_18.

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5

Maani, Kambiz. "System Dynamics and Organizational Learning." In System Dynamics, 417–30. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-4939-8790-0_543.

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6

Erkens, Gijsbert. "Dynamics of Coordination in Collaboration." In Dialogic Learning, 191–216. Dordrecht: Springer Netherlands, 2004. http://dx.doi.org/10.1007/1-4020-1931-9_10.

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7

Tallet, Jessica. "Memory Dynamics." In Encyclopedia of the Sciences of Learning, 2166–69. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_1691.

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8

Honkapohja, Seppo. "Learning Dynamics: Complete and Incomplete Learning." In Advances in Macroeconomic Theory, 239–54. London: Palgrave Macmillan UK, 2001. http://dx.doi.org/10.1057/9780333992753_12.

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9

Darandari, Eqbal, and Anne Murphy. "Assessment of Student Learning." In Higher Education Dynamics, 61–71. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6321-0_6.

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10

Wunder, Michael, and Michael Littman. "Multiagent Q-Learning Dynamics." In Encyclopedia of the Sciences of Learning, 2356–59. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_1706.

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Тези доповідей конференцій з теми "Dynamics of learning"

1

Semenikhin, S. V., and L. A. Denisova. "Learning to rank based on modified genetic algorithm." In 2016 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2016. http://dx.doi.org/10.1109/dynamics.2016.7819080.

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2

Semenikhin, Sviatoslav, and Liudmila Denisova. "Learning to rank based on multi-criteria optimization." In 2017 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2017. http://dx.doi.org/10.1109/dynamics.2017.8239503.

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3

Lohl, T., S. Pegel, K. U. Klatt, S. Engell, Chr Schmid, and A. Ali. "Dynamit — Learning system dynamics using multimedia." In 1999 European Control Conference (ECC). IEEE, 1999. http://dx.doi.org/10.23919/ecc.1999.7099309.

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4

Bobkov, Vladimir, Anastasya Bobkova, Sergey Porshnev, and Vasily Zuzin. "The application of ensemble learning for delineation of the left ventricle on echocardiographic records." In 2016 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2016. http://dx.doi.org/10.1109/dynamics.2016.7818984.

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5

Abdufattokhov, Shokhjakhon, and Behzod Muhiddinov. "Stochastic Approach for System Identification using Machine Learning." In 2019 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2019. http://dx.doi.org/10.1109/dynamics47113.2019.8944452.

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6

Hemati, Maziar. "Learning Wake Regimes from Snapshot Data." In 46th AIAA Fluid Dynamics Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2016. http://dx.doi.org/10.2514/6.2016-3781.

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7

Porshnev, S. V., A. O. Bobkova, V. V. Zyuzin, A. A. Mukhtarov, D. M. Akhmetov, and M. A. Chernyshev. "Estimation of volume of the left ventricle on MRT-images of a two-chamber projection of heart on a short axis based on deep learning." In 2017 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2017. http://dx.doi.org/10.1109/dynamics.2017.8239495.

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8

Hennes, Daniel, Karl Tuyls, and Matthias Rauterberg. "Formalizing Multi-state Learning Dynamics." In 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. IEEE, 2008. http://dx.doi.org/10.1109/wiiat.2008.33.

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Kawamoto, Kazuhiko, Yoshiyuki Tomura, and Kazushi Okamoto. "Learning pedestrian dynamics with kriging." In 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS). IEEE, 2016. http://dx.doi.org/10.1109/icis.2016.7550877.

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10

Camoriano, Raffaello, Silvio Traversaro, Lorenzo Rosasco, Giorgio Metta, and Francesco Nori. "Incremental semiparametric inverse dynamics learning." In 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016. http://dx.doi.org/10.1109/icra.2016.7487177.

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Звіти організацій з теми "Dynamics of learning"

1

Farber, Henry, and Robert Gibbons. Learning and Wage Dynamics. Cambridge, MA: National Bureau of Economic Research, July 1991. http://dx.doi.org/10.3386/w3764.

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2

Gourinchas, Pierre-Olivier, and Aaron Tornell. Exchange Rate Dynamics and Learning. Cambridge, MA: National Bureau of Economic Research, April 1996. http://dx.doi.org/10.3386/w5530.

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3

Gervais, Martin, Nir Jaimovich, Henry Siu, and Yaniv Yedid-Levi. Technological Learning and Labor Market Dynamics. Cambridge, MA: National Bureau of Economic Research, December 2013. http://dx.doi.org/10.3386/w19767.

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4

Gourinchas, Pierre-Olivier, and Aaron Tornell. Exchange Rate Dynamics, Learning and Misperception. Cambridge, MA: National Bureau of Economic Research, December 2002. http://dx.doi.org/10.3386/w9391.

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5

Lewis, James, Aldo Romero, Oleg Prozhdo, and Marcus Hanwell. Machine-Learning for Excited-State Dynamics. Office of Scientific and Technical Information (OSTI), March 2022. http://dx.doi.org/10.2172/1848053.

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6

Michelle, Kaffenberger. A Typology of Learning Profiles: Tools for Analysing the Dynamics of Learning. Research on Improving Systems of Education (RISE), December 2019. http://dx.doi.org/10.35489/bsg-rise-ri_2019/013.

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7

Leen, Todd K. Stochastic Learning Dynamics and Non-Linear Dimension Reduction. Fort Belvoir, VA: Defense Technical Information Center, March 1994. http://dx.doi.org/10.21236/ada292818.

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8

Morrow, John, and Michael Carter. Left, Right, Left: Income, Learning and Political Dynamics. Cambridge, MA: National Bureau of Economic Research, October 2013. http://dx.doi.org/10.3386/w19498.

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9

Jagannathan, Ravi, and Binying Liu. Dividend Dynamics, Learning, and Expected Stock Index Returns. Cambridge, MA: National Bureau of Economic Research, September 2015. http://dx.doi.org/10.3386/w21557.

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10

Eaton, Jonathan, Marcela Eslava, David Jinkins, C. Krizan, and James Tybout. A Search and Learning Model of Export Dynamics. Cambridge, MA: National Bureau of Economic Research, July 2021. http://dx.doi.org/10.3386/w29100.

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