Relevant bibliographies by topics / Learning by discovering

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Learning by discovering'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Learning by discovering"

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Zupan, Blaž, Marko Bohanec, Janez Demšar, and Ivan Bratko. "Learning by discovering concept hierarchies." Artificial Intelligence 109, no. 1-2 (1999): 211–42. http://dx.doi.org/10.1016/s0004-3702(99)00008-9.

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Weber, James. "Discovering Through Experimental Learning Projects." Proceedings of the International Association for Business and Society 5 (1994): 1183–88. http://dx.doi.org/10.5840/iabsproc19945106.

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Talabis, Mark Ryan del Moral. "Honeynet Learning: Discovering Information Security." Information Systems Security 15, no. 6 (2006): 56–63. http://dx.doi.org/10.1080/10658980601052019.

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Pataraia, Nino, Anoush Margaryan, Isobel Falconer, Allison Littlejohn, and Jennifer Falconer. "Discovering academics' key learning connections." Journal of Workplace Learning 26, no. 1 (2014): 56–72. http://dx.doi.org/10.1108/jwl-03-2013-0012.

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Purpose – The aim of this exploratory study is to investigate the role of personal networks in supporting academics' professional learning. In particular, the paper examines the composition of academics' networks and the implications of network tendencies for academics' learning about teaching. Design/methodology/approach – The study adopts a mixed-methods approach. Firstly, the composition of academics' networks is examined using social network analysis. Secondly, the role of these networks in academics' learning about teaching is analysed through semi-structured interviews. Findings – Findin
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Wang, Lidong, and Cheryl Ann Alexander. "Machine Learning in Big Data." International Journal of Mathematical, Engineering and Management Sciences 1, no. 2 (2016): 52–61. http://dx.doi.org/10.33889/ijmems.2016.1.2-006.

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Machine learning is an artificial intelligence method of discovering knowledge for making intelligent decisions. Big Data has great impacts on scientific discoveries and value creation. This paper introduces methods in machine learning, main technologies in Big Data, and some applications of machine learning in Big Data. Challenges of machine learning applications in Big Data are discussed. Some new methods and technology progress of machine learning in Big Data are also presented.
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Adams, David, H. K. Colebatch, and Christopher K. Walker. "Learning about Learning: Discovering the Work of Policy." Australian Journal of Public Administration 74, no. 2 (2015): 101–11. http://dx.doi.org/10.1111/1467-8500.12119.

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Wang, Lidong. "Machine Learning in Big Data." International Journal of Advances in Applied Sciences 4, no. 4 (2015): 117. http://dx.doi.org/10.11591/ijaas.v4.i4.pp117-123.

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<p>Machine learning is an artificial intelligence method of discovering knowledge for making intelligent decisions. Big Data has great impacts on scientific discoveries and value creation. This paper introduces methods in machine learning, main technologies in Big Data, and some applications of machine learning in Big Data. Challenges of machine learning applications in Big Data are discussed. Some new methods and technology progress of machine learning in Big Data are also presented.</p>
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Gershman, Samuel J., Kenneth A. Norman, and Yael Niv. "Discovering latent causes in reinforcement learning." Current Opinion in Behavioral Sciences 5 (October 2015): 43–50. http://dx.doi.org/10.1016/j.cobeha.2015.07.007.

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Gelfer, Jeffrey I. "Discovering and learning art with blocks." Day Care & Early Education 17, no. 4 (1990): 21–24. http://dx.doi.org/10.1007/bf01623194.

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Kordík, Pavel, Jan Černý, and Tomáš Frýda. "Discovering predictive ensembles for transfer learning and meta-learning." Machine Learning 107, no. 1 (2017): 177–207. http://dx.doi.org/10.1007/s10994-017-5682-0.

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Dissertations / Theses on the topic "Learning by discovering"

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Harrison, Matthew T. "Discovering compositional structure /." View online version; access limited to Brown University users, 2005. http://wwwlib.umi.com/dissertations/fullcit/3174618.

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Thesis (Ph.D.)--Brown University, 2005.<br>Vita. Thesis advisor: Stuart Geman. Includes bibliographical references (leaves 7-9, 31-33, 64-68, 107-107, 131-132, 155-157, 267-268). Also available online.
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Hengst, Bernhard Computer Science &amp Engineering Faculty of Engineering UNSW. "Discovering hierarchy in reinforcement learning." Awarded by:University of New South Wales. Computer Science and Engineering, 2003. http://handle.unsw.edu.au/1959.4/20497.

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This thesis addresses the open problem of automatically discovering hierarchical structure in reinforcement learning. Current algorithms for reinforcement learning fail to scale as problems become more complex. Many complex environments empirically exhibit hierarchy and can be modeled as interrelated subsystems, each in turn with hierarchic structure. Subsystems are often repetitive in time and space, meaning that they reoccur as components of different tasks or occur multiple times in different circumstances in the environment. A learning agent may sometimes scale to larger problems if it suc
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Perez, Santiago(Perez Lastra). "Deep learning for discovering new product opportunities." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113176.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 65-66).<br>Modeling customer preferences over multivariate attributes has long been an endeavor of marketing research. We provide an updated approach which can learn customer preferences for complex products with multiple multivariate attributes using modern Deep Neural Networks. In turn we outline approaches for framing managerial questions in the form of inference problems. With our empir
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Wallace, William Scott. "Portraits discovering art as a transformative learning process at mid-life /." [Yellow Springs, Ohio] : Antioch University, 2007. http://www.ohiolink.edu/etd/send-pdf.cgi/Wallace%20William%20Scott.pdf?acc_num=antioch1208450699.

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Thesis (Ph.D.)--Antioch University, 2007.<br>Title from PDF t.p. (viewed Apr. 29, 2008). Advisor: Carolyn Kenny, PhD. "Submitted to the Ph.D. in Leadership & Change Program of Antioch University in partial fulfillment of the requirements for the degree of Doctor of Philosophy October, 2007"--from the title page. Keywords: transformative learning, middle-age, portraiture; artists, phenomenology, Jungian psychology, midlife, depth psychology, life change. Includes bibliographical references (p. 265-273).
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Torner, Tom. "Discovering meaning : an essay on the theme of creation." Thesis, University of East Anglia, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327813.

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Farrash, Majed. "Machine learning ensemble method for discovering knowledge from big data." Thesis, University of East Anglia, 2016. https://ueaeprints.uea.ac.uk/59367/.

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Big data, generated from various business internet and social media activities, has become a big challenge to researchers in the field of machine learning and data mining to develop new methods and techniques for analysing big data effectively and efficiently. Ensemble methods represent an attractive approach in dealing with the problem of mining large datasets because of their accuracy and ability of utilizing the divide-and-conquer mechanism in parallel computing environments. This research proposes a machine learning ensemble framework and implements it in a high performance computing envir
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Sweidan, Dirar. "A General Framework for Discovering Multiple Data Groupings." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-38047.

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Clustering helps users gain insights from their data by discovering hidden structures in an unsupervised way. Unlike classification tasks that are evaluated using well-defined target labels, clustering is an intrinsically subjective task as it depends on the interpretation, need and interest of users. In many real-world applications, multiple meaningful clusterings can be hidden in the data, and different users are interested in exploring different perspectives and use cases of this same data. Despite this, most existing clustering techniques only attempt to produce a single clustering of the
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Qian, Weizhu. "Discovering human mobility from mobile data : probabilistic models and learning algorithms." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCA025.

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Les données d'utilisation des smartphones peuvent être utilisées pour étudier la mobilité humaine que ce soit en environnement extérieur ouvert ou à l'intérieur de bâtiments. Dans ce travail, nous étudions ces deux aspects de la mobilité humaine en proposant des algorithmes de machine learning adapté aux sources d'information disponibles dans chacun des contextes.Pour l'étude de la mobilité en environnement extérieur, nous utilisons les données de coordonnées GPS collectées pour découvrir les schémas de mobilité quotidiens des utilisateurs. Pour cela, nous proposons un algorithme de clustering
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Wallace, William Scott. "Portraits: Discovering Art as a Transformative Learning Process at Mid-Life." Antioch University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=antioch1208450699.

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Floderus, Sebastian, and Linus Rosenholm. "An educational experiment in discovering spear phishing attacks." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18446.

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Background: Spear phishing attacks uses social engineering targeting a specific person to steal credential information or infect the users computer with malware. It is often done through emails and it can be very hard to spot the difference between a legitimate email and a scam email. Cybercrime is a growing problem and there is many ways to inform and educate individuals on the subject.Objectives: This study intends to perform an experiment to see if an educationalsupport tool can be used to better identify phishing emails. Furthermore see if there is a difference in susceptibility between st
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Books on the topic "Learning by discovering"

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Bargar, June R. Discovering learning preferences and learning differences in the classroom. Ohio Agricultural Education Curriculum Materials Service, Ohio State University, 1994.

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Edwards, Roy. Discovering electronics: An active-learning approach. DP, 1994.

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Malamud, Ofer. Discovering one's talent: Learning from academic specialization. National Bureau of Economic Research, 2009.

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Malamud, Ofer. Discovering one's talent: Learning from academic specialization. National Bureau of Economic Research, 2009.

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Sally, Machlis, ed. Discovering Northwest volcanoes: A learning and activity book. Dog-Eared Publications, 1993.

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Morris, Paul, and Ference Marton. What matters?: Discovering critical conditions of classroom learning. Acta Universitatis Gothoburgensis, 2002.

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James, Rebecca. Discovering HighScope: A teacher's journal. HighScope Press, a division of the HighScope Educational Research Foundation, 2013.

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The open mind: Discovering the 6 patterns of natural intelligence. Conari Press, 1996.

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Sherfield, Robert M. Cornerstone: Discovering your potential, learning actively, and living well. 5th ed. Pearson Prentice Hall, 2008.

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Sambhava, Luvmour, ed. Natural learning rhythms: Discovering how and when your child learns. Celestial Arts, 1993.

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Book chapters on the topic "Learning by discovering"

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Myllyntaus, Timo. "Discovering Switzerland." In Travels of Learning. Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-017-3584-1_12.

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Lashkia, George V., and Laurence Anthony. "Learning by Discovering Conflicts." In Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44886-1_39.

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Lax, Sara. "Discovering Traces of the Past …" In Out There Learning, edited by Deborah Louise Curran, Cameron Owens, Helga Thorson, and Elizabeth Vibert. University of Toronto Press, 2019. http://dx.doi.org/10.3138/9781487519469-013.

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Džeroski, Sašo, and Igor Petrovski. "Discovering dynamics with genetic programming." In Machine Learning: ECML-94. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-57868-4_70.

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Ehlers, Ulf-Daniel. "Discovering the Power of Social Media." In Open Learning Cultures. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38174-4_2.

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Percy, Diana. "Discovering Your Individual Learning Style." In Adult Study Tactics. Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-11086-5_9.

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Tatti, Nikolaj, and Jilles Vreeken. "Discovering Descriptive Tile Trees." In Machine Learning and Knowledge Discovery in Databases. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33460-3_6.

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Moore, David Thornton. "Discovering the Pedagogy of Experience." In Engaged Learning in the Academy. Palgrave Macmillan US, 2013. http://dx.doi.org/10.1057/9781137025197_5.

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Papatheodorou, Christos, and Alexandra Vassiliou. "Discovering Ontologies for e-Learning Platforms." In Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11752912_72.

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Gaglio, Salvatore, Floriana Esposito, and Stefano Nolfi. "Panel Summary Perceptual Learning and Discovering." In Human and Machine Vision. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4899-1004-2_24.

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Conference papers on the topic "Learning by discovering"

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Posadas, Inmaculada, and María Pérez. "DISCOVERING PHARMACOLOGY." In 13th International Conference on Education and New Learning Technologies. IATED, 2021. http://dx.doi.org/10.21125/edulearn.2021.0847.

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Zapata, Alfredo, Victor H. Menendez, and Manuel E. Prieto. "Discovering Learning Objects Usability Characteristics." In 2009 Ninth International Conference on Intelligent Systems Design and Applications. IEEE, 2009. http://dx.doi.org/10.1109/isda.2009.184.

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Jian-Chao Chen, Qi-Lun Zheng, and Zhao Li. "Discovering Chinese Concept-In-Corpus." In 2008 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2008. http://dx.doi.org/10.1109/icmlc.2008.4620835.

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Salleb-Aouissi, Ansaf, Bert Huang, and David Waltz. "Discovering Characterization Rules from Rankings." In 2009 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2009. http://dx.doi.org/10.1109/icmla.2009.67.

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Waxmonsky, Sonjia, John Goldsmith, and Andrey Rzhetsky. "Discovering and Counting Biomedical Verbs." In 2010 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2010. http://dx.doi.org/10.1109/icmla.2010.155.

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Isa, Wan Abdul Rahim Wan Mohd, Azwah Mas Sohor, and Intan Noratika Mat Sah. "Discovering drivers of using M-learning." In 2016 4th International Conference on User Science and Engineering (i-USEr). IEEE, 2016. http://dx.doi.org/10.1109/iuser.2016.7857968.

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Jenkins, Ronald P., Sawyer D. Campbell, Douglas H. Werner, and Pingjuan L. Werner. "Discovering metasurface robustness through deep learning." In Metamaterials, Metadevices, and Metasystems 2021, edited by Nader Engheta, Mikhail A. Noginov, and Nikolay I. Zheludev. SPIE, 2021. http://dx.doi.org/10.1117/12.2593465.

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Huang, Jun, Linchuan Xu, Jing Wang, Lei Feng, and Kenji Yamanishi. "Discovering Latent Class Labels for Multi-Label Learning." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/423.

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Existing multi-label learning (MLL) approaches mainly assume all the labels are observed and construct classification models with a fixed set of target labels (known labels). However, in some real applications, multiple latent labels may exist outside this set and hide in the data, especially for large-scale data sets. Discovering and exploring the latent labels hidden in the data may not only find interesting knowledge but also help us to build a more robust learning model. In this paper, a novel approach named DLCL (i.e., Discovering Latent Class Labels for MLL) is proposed which can not onl
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Ding Zhou, Yang Song, Hongyuan Zha, and Ya Zhang. "Towards discovering organizational structure from email corpus." In Fourth International Conference on Machine Learning and Applications. IEEE, 2005. http://dx.doi.org/10.1109/icmla.2005.66.

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Li, Bai, Jing Yi Xie, and Frank Rudzicz. "Representation Learning for Discovering Phonemic Tone Contours." In Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology. Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.sigmorphon-1.26.

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Reports on the topic "Learning by discovering"

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Malamud, Ofer. Discovering One's Talent: Learning from Academic Specialization. National Bureau of Economic Research, 2009. http://dx.doi.org/10.3386/w15522.

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Fitzpatrick, Paul. Object Lesson: Discovering and Learning to Recognize Objects. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada434695.

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Vesselinov, Velimir, Bulbul Ahmmed, Maruti Mudunuru, et al. Discovering Hidden Geothermal Signatures using Unsupervised Machine Learning. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1781347.

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