Auswahl der wissenschaftlichen Literatur zum Thema „The learning space“
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Zeitschriftenartikel zum Thema "The learning space":
Talbert, Robert, und Anat Mor-Avi. „A space for learning: An analysis of research on active learning spaces“. Heliyon 5, Nr. 12 (Dezember 2019): e02967. http://dx.doi.org/10.1016/j.heliyon.2019.e02967.
Fabisch, Alexander, Yohannes Kassahun, Hendrik Wöhrle und Frank Kirchner. „Learning in compressed space“. Neural Networks 42 (Juni 2013): 83–93. http://dx.doi.org/10.1016/j.neunet.2013.01.020.
Sedlmeier, Andreas, und Sebastian Feld. „Learning indoor space perception“. Journal of Location Based Services 12, Nr. 3-4 (02.10.2018): 179–214. http://dx.doi.org/10.1080/17489725.2018.1539255.
Martland, Rebecca. „Space to lead learning“. Early Years Educator 21, Nr. 11 (02.03.2020): 18–20. http://dx.doi.org/10.12968/eyed.2020.21.11.18.
Williamson, Andy, und Carolyn Nodder. „Extending the learning space“. ACM SIGCAS Computers and Society 32, Nr. 3 (30.09.2002): 1. http://dx.doi.org/10.1145/644618.644620.
Prade, Henri, und Mathieu Serrurier. „Bipolar version space learning“. International Journal of Intelligent Systems 23, Nr. 10 (Oktober 2008): 1135–52. http://dx.doi.org/10.1002/int.20310.
Hughes, Billie, Barry Kort und Jim Walters. „Virtual space learning MariMUSE“. ACM SIGCUE Outlook 22, Nr. 2 (April 1994): 17–22. http://dx.doi.org/10.1145/182815.182817.
Wible, B. „Learning to Share Space“. Science 331, Nr. 6020 (24.02.2011): 988. http://dx.doi.org/10.1126/science.331.6020.988-a.
Guan, Renchu, Xu Wang, Maurizio Marchese, Mary Qu Yang, Yanchun Liang und Chen Yang. „Feature space learning model“. Journal of Ambient Intelligence and Humanized Computing 10, Nr. 5 (09.05.2018): 2029–40. http://dx.doi.org/10.1007/s12652-018-0805-4.
Hillstrom-Svercek, Sandra. „Space: A learning center“. Day Care & Early Education 12, Nr. 4 (Juni 1985): 31–36. http://dx.doi.org/10.1007/bf01619854.
Dissertationen zum Thema "The learning space":
Ameur, Foued ben Fredj. „Space-bounded learning algorithms /“. Paderborn : Heinz Nixdorf Inst, 1996. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=007171235&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.
Kiddle, Rebecca. „Learning outside the box : designing social learning space“. Thesis, Oxford Brookes University, 2011. https://radar.brookes.ac.uk/radar/items/f7b36f17-cf4f-4590-8dd7-e6df3ecfc1d2/1/.
Ferreira, Paulo Victor Rodrigues. „SRML: Space Radio Machine Learning“. Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-dissertations/199.
Chardonnet, Lucile. „A Shared Learning Space inMidsommarkransen“. Thesis, KTH, Arkitektur, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-223240.
Kumar, Shailesh. „Modular learning through output space decomposition /“. Full text (PDF) from UMI/Dissertation Abstracts International, 2000. http://wwwlib.umi.com/cr/utexas/fullcit?p3004308.
Qian, Jing. „Unsupervised learning in high-dimensional space“. Thesis, Boston University, 2014. https://hdl.handle.net/2144/12951.
In machine learning, the problem of unsupervised learning is that of trying to explain key features and find hidden structures in unlabeled data. In this thesis we focus on three unsupervised learning scenarios: graph based clustering with imbalanced data, point-wise anomaly detection and anomalous cluster detection on graphs. In the first part we study spectral clustering, a popular graph based clustering technique. We investigate the reason why spectral clustering performs badly on imbalanced and proximal data. We then propose the partition constrained minimum cut (PCut) framework based on a novel parametric graph construction method, that is shown to adapt to different degrees of imbalanced data. We analyze the limit cut behavior of our approach, and demonstrate the significant performance improvement through clustering and semi-supervised learning experiments on imbalanced data. [TRUNCATED]
Nichols, B. „Reinforcement learning in continuous state- and action-space“. Thesis, University of Westminster, 2014. https://westminsterresearch.westminster.ac.uk/item/967w8/reinforcement-learning-in-continuous-state-and-action-space.
Saeed, Sabina, und Sabina Saeed. „Learning To Learn: A Look Into the Collaborative Learning Space“. Thesis, The University of Arizona, 2017. http://hdl.handle.net/10150/625142.
Mackevicius, Emily Lambert. „Building a state space for song learning“. Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120871.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 159-177).
Song learning circuitry is thought to operate using a unique representation of each moment within each song syllable. Distinct timestamps for each moment in the song have been observed in the premotor cortical nucleus HVC, where neurons burst in sparse sequences. However, such sparse sequences are not present in very young birds, which sing highly variable syllables of random lengths. Furthermore, young birds learn by imitating a tutor song, and it was previously unclear precisely how the experience of hearing a tutor might shape auditory, motor, and evaluation pathways in the songbird brain. My thesis presents a framework for how these pathways may assemble during early learning, using simple neural mechanisms. I start with a neural network model for how premotor sequences may grow and split. This model predicts that the sequence-generating nucleus HVC would receive rhythmically patterned training inputs. I found such a signal when I recorded neurons that project to HVC. When juvenile birds sing, these neurons burst at the beginning of each syllable, and when the birds listen to a tutor, neurons burst at the rhythm of the tutor's song. Bursts marking the beginning of every tutor syllable could seed chains of sequential activity in HVC that could be used to generate the bird's own song imitation. I next used functional calcium imaging to characterize HVC sequences before and after tutor exposure. Analysis of these datasets led us to develop a new method for unsupervised detection of neural sequences. Using this method, I was able to observe neural sequences even prior to tutor exposure. Some of these sequences could be tracked as new syllables emerged after tutor exposure, and some sequences appeared to become coupled to the new syllables. In light of my new data, I expand on previous models of song learning to form a detailed hypothesis for how simple neural processes may perform song learning from start to finish.
by Emily Lambert Mackevicius.
Ph. D.
Bellocchi, Alberto. „Learning in the third space : a sociocultural perspective on learning with analogies“. Queensland University of Technology, 2009. http://eprints.qut.edu.au/30136/.
Bücher zum Thema "The learning space":
Kommers, Piet A. M., und Madhumita Bhattacharya. The Connected learning space. Chesapeake, VA: Association for the Advancement of Computing in Education, 2009.
Leordeanu, Marius. Unsupervised Learning in Space and Time. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42128-1.
Sadeghi, Sayed Hadi. Pathology of Learning in Cyber Space. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-91449-7.
Vajpeyi, Kabir. Building as learning aid: Developing school space as learning resource. New Delhi: Vinyās, Centre for Architectural Research & Design, 2005.
McDonald, Frank, und B. M. Evans. Space, place, life: Learning from place 1. Abingdon, Oxon: Routledge, 2011.
Hertzberger, Herman. Space and learning: Lessons in architecture 3. Rotterdam: 010 Publishers, 2008.
Zheng, Yefeng, und Dorin Comaniciu. Marginal Space Learning for Medical Image Analysis. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0600-0.
Marton, Ference. Classroom discourse and the space of learning. Mahwah, NJ: Erlbaum Associates, 2004.
Marriott, S. Self-organising state space decoder for reinforcement learning. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1995.
Monk, Nicholas. Open-space learning: A study in transdisciplinary pedagogy. London: Bloomsbury, 2011.
Buchteile zum Thema "The learning space":
Huang, Ronghuai, J. Michael Spector und Junfeng Yang. „Learning Space Design“. In Educational Technology, 149–64. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6643-7_9.
Zheng, Yefeng, und Dorin Comaniciu. „Marginal Space Learning“. In Marginal Space Learning for Medical Image Analysis, 25–65. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0600-0_2.
Bosch, Antal van den, Bernhard Hengst, John Lloyd, Risto Miikkulainen, Hendrik Blockeel und Hendrik Blockeel. „Hypothesis Space“. In Encyclopedia of Machine Learning, 511–13. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_373.
Bosch, Antal van den, Bernhard Hengst, John Lloyd, Risto Miikkulainen, Hendrik Blockeel und Hendrik Blockeel. „Hypothesis Space“. In Encyclopedia of Machine Learning, 513. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_374.
Utgoff, Paul E., James Cussens, Stefan Kramer, Sanjay Jain, Frank Stephan, Luc De Raedt, Ljupčo Todorovski et al. „Instance Space“. In Encyclopedia of Machine Learning, 549. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_408.
Fürnkranz, Johannes, Philip K. Chan, Susan Craw, Claude Sammut, William Uther, Adwait Ratnaparkhi, Xin Jin et al. „Model Space“. In Encyclopedia of Machine Learning, 683. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_552.
Blockeel, Hendrik, Geoffrey I. Webb, Peter Auer und Geoffrey I. Webb. „Object Space“. In Encyclopedia of Machine Learning, 733. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_607.
Lagoudakis, Michail G., Thomas Zeugmann und Claude Sammut. „Version Space“. In Encyclopedia of Machine Learning, 1024–25. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_877.
Kapitzke, Cushla, und Peter D. Renshaw. „Third Space in Cyberspace“. In Dialogic Learning, 45–61. Dordrecht: Springer Netherlands, 2004. http://dx.doi.org/10.1007/1-4020-1931-9_3.
Falmagne, Jean-Claude, und Jean-Paul Doignon. „Building a Knowledge Space“. In Learning Spaces, 297–333. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-01039-2_15.
Konferenzberichte zum Thema "The learning space":
McCrone, Luke. „Transitional space: learning in the spaces in-between“. In Learning Connections 2019: Spaces, People, Practice. University College Cork||National Forum for the Enhancement of Teaching and Learning in Higher Education, 2019. http://dx.doi.org/10.33178/lc2019.14.
Hughes, Billie, Jim Walters und Barry Kort. „Virtual space learning“. In the 1994 ACM symposium. New York, New York, USA: ACM Press, 1994. http://dx.doi.org/10.1145/326619.326949.
Alison, Chan See Mun, Umeda Kyoko und Nozaki Hironari. „Student Learning Space“. In the 2019 3rd International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3345120.3345192.
Cardenas, Jeffery. „Enhancing Higher Education Science & Technology Learning through Exploration“. In Space 2006. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2006. http://dx.doi.org/10.2514/6.2006-7301.
Al-Stouhi, S., C. K. Reddy und D. E. Lanfear. „Label Space Transfer Learning“. In 2012 IEEE 24th International Conference on Tools with Artificial Intelligence (ICTAI 2012). IEEE, 2012. http://dx.doi.org/10.1109/ictai.2012.103.
Peters, J., und S. Schaal. „Learning Operational Space Control“. In Robotics: Science and Systems 2006. Robotics: Science and Systems Foundation, 2006. http://dx.doi.org/10.15607/rss.2006.ii.033.
Blanc, Anja Le, Jonathan Bunt, Jim Petch und Yien Kwok. „The virtual learning space“. In the tenth international conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1050491.1050505.
Lin, Hsiu-Chin, Matthew Howard und Sethu Vijayakumar. „Learning null space projections“. In 2015 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2015. http://dx.doi.org/10.1109/icra.2015.7139551.
Fan, Zhou, Rui Su, Weinan Zhang und Yong Yu. „Hybrid Actor-Critic Reinforcement Learning in Parameterized Action Space“. In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/316.
van den Dool, Jaco, und Bert van Uffelen. „MUSICAL SAFE SPACE“. In International Conference on Education and New Learning Technologies. IATED, 2016. http://dx.doi.org/10.21125/edulearn.2016.1029.
Berichte der Organisationen zum Thema "The learning space":
Schultz, Alan C. Adapting the Evaluation Space to Improve Global Learning,. Fort Belvoir, VA: Defense Technical Information Center, Januar 1991. http://dx.doi.org/10.21236/ada294069.
Koedinger, Kenneth R., Daniel D. Suthers und Kenneth D. Forbus. Component-Based Construction of a Science Learning Space. Fort Belvoir, VA: Defense Technical Information Center, Januar 1998. http://dx.doi.org/10.21236/ada638366.
Guttromson, Ross, Stephen Verzi, Christian Jones, Asael Sorensen, Raymond Byrne und Charles Hanley. Grid Stability Using Machine Learning State Space Navigation. Office of Scientific and Technical Information (OSTI), Juli 2019. http://dx.doi.org/10.2172/1762943.
Boyd, Zachary M., und Joanne Roth Wendelberger. An Integrated Approach to Parameter Learning in Infinite-Dimensional Space. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1392846.
Pearlmutter, Barak A. Learning State Space Trajectories in Recurrent Neural Networks: A preliminary Report. Fort Belvoir, VA: Defense Technical Information Center, Juli 1988. http://dx.doi.org/10.21236/ada219114.
Qi, Fei, Zhaohui Xia, Gaoyang Tang, Hang Yang, Yu Song, Guangrui Qian, Xiong An, Chunhuan Lin und Guangming Shi. A Graph-based Evolutionary Algorithm for Automated Machine Learning. Web of Open Science, Dezember 2020. http://dx.doi.org/10.37686/ser.v1i2.77.
Ahmed AlGarf, Yasmine. From Self-Awareness to Purposeful Employment: Guiding Egyptian youth using arts-based learning. Oxfam IBIS, August 2021. http://dx.doi.org/10.21201/2021.7932.
Niyogi, Partha, und Robert C. Berwick. Formalizing Triggers: A Learning Model for Finite Spaces. Fort Belvoir, VA: Defense Technical Information Center, November 1993. http://dx.doi.org/10.21236/ada276776.
Fukumizu, Kenji, Francis R. Bach und Michael I. Jordan. Dimensionality Reduction for Supervised Learning With Reproducing Kernel Hilbert Spaces. Fort Belvoir, VA: Defense Technical Information Center, Mai 2003. http://dx.doi.org/10.21236/ada446572.
Lemcke-Kibby, Allison. Utilizing Affinity Spaces and Critical Literacies for Multi-Media Language Learning. Portland State University Library, Mai 2013. http://dx.doi.org/10.15760/honors.26.