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Artykuły w czasopismach na temat "Approaches to learning"
Urhahne, Detlef. "Learning approaches". Educational Psychology 40, nr 5 (27.05.2020): 533–34. http://dx.doi.org/10.1080/01443410.2020.1755503.
Pełny tekst źródłaWiltshire, Monica. "Approaches to learning". Early Years Educator 17, nr 11 (2.03.2016): 28–30. http://dx.doi.org/10.12968/eyed.2016.17.11.28.
Pełny tekst źródłaDuff, Angus, i Sam McKinstry. "Students' Approaches to Learning". Issues in Accounting Education 22, nr 2 (1.05.2007): 183–214. http://dx.doi.org/10.2308/iace.2007.22.2.183.
Pełny tekst źródłaWasson, Barbara, i Paul A. Kirschner. "Learning Design: European Approaches". TechTrends 64, nr 6 (13.05.2020): 815–27. http://dx.doi.org/10.1007/s11528-020-00498-0.
Pełny tekst źródłaAlbergaria-Almeida, Patrícia, José Joaquim Teixeira-Dias, Mariana Martinho i Chinthaka Balasooriya. "Kolb’s Learning Styles and Approaches to Learning". International Journal of Knowledge Society Research 1, nr 3 (lipiec 2010): 1–16. http://dx.doi.org/10.4018/jksr.2010070101.
Pełny tekst źródłaCuthbert, Peter F. "The student learning process: Learning styles or learning approaches?" Teaching in Higher Education 10, nr 2 (kwiecień 2005): 235–49. http://dx.doi.org/10.1080/1356251042000337972.
Pełny tekst źródłaSharma, Divesh S. "Accounting students' learning conceptions, approaches to learning, and the influence of the learning–teaching context on approaches to learning". Accounting Education 6, nr 2 (czerwiec 1997): 125–46. http://dx.doi.org/10.1080/096392897331532.
Pełny tekst źródłaRajaratnam, Navin, i SuzanneMaria D′cruz. "Learning styles and learning approaches - Are they different?" Education for Health 29, nr 1 (2016): 59. http://dx.doi.org/10.4103/1357-6283.178924.
Pełny tekst źródłaAhamad, Maksud, i Nesar Ahmad. "Machine Learning Approaches to Digital Learning Performance Analysis". International Journal of Computing and Digital Systems 10, nr 1 (25.11.2021): 963–71. http://dx.doi.org/10.12785/ijcds/100187.
Pełny tekst źródłaBattou, Amal, Omar Baz i Driss Mammass. "Learning Design Approaches for Designing Virtual Learning Environments". Communications on Applied Electronics 5, nr 9 (26.09.2016): 31–37. http://dx.doi.org/10.5120/cae2016652369.
Pełny tekst źródłaRozprawy doktorskie na temat "Approaches to learning"
Potari, Despina. "Learning approaches in mathematics". Thesis, University of Edinburgh, 1987. http://hdl.handle.net/1842/12130.
Pełny tekst źródłaHussein, Ahmed. "Deep learning based approaches for imitation learning". Thesis, Robert Gordon University, 2018. http://hdl.handle.net/10059/3117.
Pełny tekst źródłaEffraimidis, Dimitros. "Computation approaches for continuous reinforcement learning problems". Thesis, University of Westminster, 2016. https://westminsterresearch.westminster.ac.uk/item/q0y82/computation-approaches-for-continuous-reinforcement-learning-problems.
Pełny tekst źródłaChang, Yu-Han Ph D. Massachusetts Institute of Technology. "Approaches to multi-agent learning". Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33932.
Pełny tekst źródłaIncludes bibliographical references (leaves 165-171).
Systems involving multiple autonomous entities are becoming more and more prominent. Sensor networks, teams of robotic vehicles, and software agents are just a few examples. In order to design these systems, we need methods that allow our agents to autonomously learn and adapt to the changing environments they find themselves in. This thesis explores ideas from game theory, online prediction, and reinforcement learning, tying them together to work on problems in multi-agent learning. We begin with the most basic framework for studying multi-agent learning: repeated matrix games. We quickly realize that there is no such thing as an opponent-independent, globally optimal learning algorithm. Some form of opponent assumptions must be necessary when designing multi-agent learning algorithms. We first show that we can exploit opponents that satisfy certain assumptions, and in a later chapter, we show how we can avoid being exploited ourselves. From this beginning, we branch out to study more complex sequential decision making problems in multi-agent systems, or stochastic games. We study environments in which there are large numbers of agents, and where environmental state may only be partially observable.
(cont.) In fully cooperative situations, where all the agents receive a single global reward signal for training, we devise a filtering method that allows each individual agent to learn using a personal training signal recovered from this global reward. For non-cooperative situations, we introduce the concept of hedged learning, a combination of regret-minimizing algorithms with learning techniques, which allows a more flexible and robust approach for behaving in competitive situations. We show various performance bounds that can be guaranteed with our hedged learning algorithm, thus preventing our agent from being exploited by its adversary. Finally, we apply some of these methods to problems involving routing and node movement in a mobilized ad-hoc networking domain.
by Yu-Han Chang.
Ph.D.
Flaherty, Drew. "Artistic approaches to machine learning". Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/200191/1/Drew_Flaherty_Thesis.pdf.
Pełny tekst źródłaYu, Kai. "Statistical Learning Approaches to Information Filtering". Diss., lmu, 2004. http://nbn-resolving.de/urn:nbn:de:bvb:19-25120.
Pełny tekst źródłaKashima, Hisashi. "Machine learning approaches for structured data". 京都大学 (Kyoto University), 2007. http://hdl.handle.net/2433/135953.
Pełny tekst źródłaChen, Zhe Haykin Simon S. "Stochastic approaches for correlation-based learning". *McMaster only, 2004.
Znajdź pełny tekst źródłaBoots, Byron. "Spectral Approaches to Learning Predictive Representations". Research Showcase @ CMU, 2012. http://repository.cmu.edu/dissertations/131.
Pełny tekst źródłaPellegrini, Giovanni. "Relational Learning approaches for Recommender Systems". Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/318892.
Pełny tekst źródłaKsiążki na temat "Approaches to learning"
Stobie, Tristian D. Approaches to learning. Petersfield: European Council of International Schools., 1997.
Znajdź pełny tekst źródłaApproaches to learning. Ypsilanti, Mich: Highscope Press, 2012.
Znajdź pełny tekst źródłaKim, Reid D., Hresko Wayne P i Swanson H. Lee 1947-, red. Cognitive approaches to learning disabilities. Wyd. 3. Austin, TX: Pro-Ed, 1996.
Znajdź pełny tekst źródłaHolistic approaches to language learning. Frankfurt am Main: P. Lang, 2005.
Znajdź pełny tekst źródłaInc, ebrary, red. Machine learning approaches to bioinformatics. Singapore: World Scientific, 2010.
Znajdź pełny tekst źródła1943-, Kueker D. W., i Smith Carl H. 1950-, red. Learning and geometry: Computational approaches. Boston: Birkhäuser, 1996.
Znajdź pełny tekst źródłaHarteis, Christian, David Gijbels i Eva Kyndt, red. Research Approaches on Workplace Learning. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89582-2.
Pełny tekst źródłaCutting, Roger, i Rowena Passy, red. Contemporary Approaches to Outdoor Learning. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85095-1.
Pełny tekst źródłaTouretzky, David, red. Connectionist Approaches to Language Learning. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4008-3.
Pełny tekst źródłaKueker, David W., i Carl H. Smith, red. Learning and Geometry: Computational Approaches. Boston, MA: Birkhäuser Boston, 1996. http://dx.doi.org/10.1007/978-1-4612-4088-4.
Pełny tekst źródłaCzęści książek na temat "Approaches to learning"
Singh, Nirbhay N., Diane E. D. Deitz i Judy Singh. "Behavioral Approaches". W Learning Disabilities, 375–414. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4613-9133-3_13.
Pełny tekst źródłaDaelemans, Walter. "Machine Learning Approaches". W Text, Speech and Language Technology, 285–304. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-015-9273-4_17.
Pełny tekst źródłaYang, Ching-Chi, i Lih-Yuan Deng. "Statistical Learning Approaches". W Dimensionality Reduction in Data Science, 169–77. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05371-9_8.
Pełny tekst źródłaVenugopal, Deepak, i Max Garzon. "Machine Learning Approaches". W Dimensionality Reduction in Data Science, 179–97. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05371-9_9.
Pełny tekst źródłaAbbott, Tina. "Social learning approaches". W Social and Personality Development, 51–66. London: Routledge, 2021. http://dx.doi.org/10.4324/9781003209300-6.
Pełny tekst źródłaLiu, Han, Alexander Gegov i Mihaela Cocea. "Ensemble Learning Approaches". W Studies in Big Data, 63–73. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23696-4_6.
Pełny tekst źródłaMisra, Pradeep Kumar. "Approaches to Learning". W Learning and Teaching for Teachers, 17–36. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3077-4_2.
Pełny tekst źródłaKim, Rina, i Lillie R. Albert. "Methodological Approaches". W Mathematics Teaching and Learning, 33–48. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13542-7_3.
Pełny tekst źródłaHengst, Bernhard. "Hierarchical Approaches". W Adaptation, Learning, and Optimization, 293–323. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27645-3_9.
Pełny tekst źródłaLavrač, Nada, Vid Podpečan i Marko Robnik-Šikonja. "Unified Representation Learning Approaches". W Representation Learning, 143–52. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68817-2_6.
Pełny tekst źródłaStreszczenia konferencji na temat "Approaches to learning"
Li, Yumeng, Pingfeng Wang i Weirong Xiao. "Uncertainty Quantification of Atomistic Materials Simulation with Machine Learning Potentials". W 2018 AIAA Non-Deterministic Approaches Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2018. http://dx.doi.org/10.2514/6.2018-2166.
Pełny tekst źródłaAnıktar, Serhat, i Ayfer Aytuğ. "DESIGNING LEARNING SPACES TO DIFFERENT LEARNING APPROACHES". W International Technology, Education and Development Conference. IATED, 2016. http://dx.doi.org/10.21125/iceri.2016.1119.
Pełny tekst źródłaSen, Gabriel, Albert Adeboye i Oluwole Alagbe. "STUDENT LEARNING APPROACHES OF ARCHITECTURE STUDENTS: DEEP OR SURFACE LEARNING APPROACH". W 14th International Technology, Education and Development Conference. IATED, 2020. http://dx.doi.org/10.21125/inted.2020.2588.
Pełny tekst źródłaYuen, Andrew. "Blended learning in economics and finance courses at business school". W International Conference on New Approaches in Education. Global, 2019. http://dx.doi.org/10.33422/icnaeducation.2019.07.394.
Pełny tekst źródłaIsaac, Benson, i Douglas L. Allaire. "A Dynamic Data-Driven Approach to Optimal Offline Learning for Online Flight Capability Estimation". W 18th AIAA Non-Deterministic Approaches Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2016. http://dx.doi.org/10.2514/6.2016-1444.
Pełny tekst źródłaYılmaz, Veysel. "Financial Machine Learning". W 4th International Symposium on Innovative Approaches in Social, Human and Administrative Sciences. SETSCI, 2019. http://dx.doi.org/10.36287/setsci.4.8.035.
Pełny tekst źródłaKhalil, Mohammad. "Probabilistic Approaches to Transfer Learning." W Proposed for presentation at the 4th annual Sandia Machine Learning and Deep Learning Workshop held July 19-22, 2021 in ,. US DOE, 2021. http://dx.doi.org/10.2172/1882483.
Pełny tekst źródłaTena Cucala, David J., Bernardo Cuenca Grau i Boris Motik. "Faithful Approaches to Rule Learning". W 19th International Conference on Principles of Knowledge Representation and Reasoning {KR-2022}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/kr.2022/50.
Pełny tekst źródłaShehu Kabir, Fatima. "Application of Unified Theory of Acceptance and Use of Technology to Learning Management System Use: A Study of Ahmadu Bello University Distance Learning Centre". W 3rd International Conference on New Approaches in Education. GLOBALKS, 2021. http://dx.doi.org/10.33422/3rd.icnaeducation.2021.07.26.
Pełny tekst źródłaWen, Xue. "MOOC Learning Outcome Prediction Using Machine Learning Approaches". W 2022 AERA Annual Meeting. Washington DC: AERA, 2022. http://dx.doi.org/10.3102/1885130.
Pełny tekst źródłaRaporty organizacyjne na temat "Approaches to learning"
Tucker, Jennifer S. Mobile Learning Approaches for U.S. Army Training. Fort Belvoir, VA: Defense Technical Information Center, sierpień 2010. http://dx.doi.org/10.21236/ada528742.
Pełny tekst źródłaVillavicencio, Xuzel, i Caitlin Coflan. Hybrid learning International experiences with multimodal approaches. EdTech Hub, lipiec 2022. http://dx.doi.org/10.53832/edtechhub.0112.
Pełny tekst źródłaGress, Gabriel. Understanding machine learning approaches for partial differential equations. Office of Scientific and Technical Information (OSTI), wrzesień 2020. http://dx.doi.org/10.2172/1669073.
Pełny tekst źródłaBhagavatula, Vijayakumar. Advanced Signal Processing and Machine Learning Approaches for EEG Analysis. Fort Belvoir, VA: Defense Technical Information Center, lipiec 2010. http://dx.doi.org/10.21236/ada535204.
Pełny tekst źródłaTillett, Will, i Oliver Jones. ‘Improving Rural Sanitation in Challenging Contexts’ Sanitation Learning Hub Learning Brief 8. The Sanitation Learning Hub, Institute of Development Studies, marzec 2021. http://dx.doi.org/10.19088/slh.2021.006.
Pełny tekst źródłaMeeker, Jessica. Mutual Learning for Policy Impact: Insights from CORE. Sharing Experience and Learning on Approaches to Influence Policy and Practice. Institute of Development Studies (IDS), sierpień 2021. http://dx.doi.org/10.19088/core.2021.005.
Pełny tekst źródłaGrogger, Jeffrey, Sean Gupta, Ria Ivandic i Tom Kirchmaier. Comparing Conventional and Machine-Learning Approaches to Risk Assessment in Domestic Abuse Cases. Cambridge, MA: National Bureau of Economic Research, grudzień 2020. http://dx.doi.org/10.3386/w28293.
Pełny tekst źródłaSihi, Debjani, Kanad Basu i Debjani Singh. Improved Understanding of Coupled Water and Carbon Cycle Processes through Machine Learning Approaches. Office of Scientific and Technical Information (OSTI), kwiecień 2021. http://dx.doi.org/10.2172/1769721.
Pełny tekst źródłaZeidenstein, Sondra, i Kirsten Moore. Learning About Sexuality: A Practical Beginning. Population Council, 1996. http://dx.doi.org/10.31899/pgy1996.1007.
Pełny tekst źródłaMa, Yue, i Felix Distel. Learning Formal Definitions for Snomed CT from Text. Technische Universität Dresden, 2013. http://dx.doi.org/10.25368/2022.193.
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