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Статті в журналах з теми "Retrieval-based learning"
Karpicke, Jeffrey D. "Retrieval-Based Learning." Current Directions in Psychological Science 21, no. 3 (May 30, 2012): 157–63. http://dx.doi.org/10.1177/0963721412443552.
Повний текст джерелаFazio, Lisa K., and Elizabeth J. Marsh. "Retrieval-Based Learning in Children." Current Directions in Psychological Science 28, no. 2 (January 7, 2019): 111–16. http://dx.doi.org/10.1177/0963721418806673.
Повний текст джерелаSivasankaran, Deepika, Sai Seena P, Rajesh R, and Madheswari Kanmani. "Sketch Based Image Retrieval using Deep Learning Based Machine Learning." International Journal of Engineering and Advanced Technology 10, no. 5 (June 30, 2021): 79–86. http://dx.doi.org/10.35940/ijeat.e2622.0610521.
Повний текст джерелаBlunt, Janell R., and Jeffrey D. Karpicke. "Learning with retrieval-based concept mapping." Journal of Educational Psychology 106, no. 3 (2014): 849–58. http://dx.doi.org/10.1037/a0035934.
Повний текст джерелаSanders, Lia Lira Olivier, Randal Pompeu Ponte, Antônio Brazil Viana Júnior, Arnaldo Aires Peixoto Junior, Marcos Kubrusly, and Antônio Miguel Furtado Leitão. "Retrieval-Based Learning in Neuroanatomy Classes." Revista Brasileira de Educação Médica 43, no. 4 (December 2019): 92–98. http://dx.doi.org/10.1590/1981-52712015v43n4rb20180184ingles.
Повний текст джерелаLi, Yueli, Rongfang Bie, Chenyun Zhang, Zhenjiang Miao, Yuqi Wang, Jiajing Wang, and Hao Wu. "Optimized learning instance-based image retrieval." Multimedia Tools and Applications 76, no. 15 (September 20, 2016): 16749–66. http://dx.doi.org/10.1007/s11042-016-3950-9.
Повний текст джерелаB, Gomathi. "Semantic Web Application in E-learning Using Protege based on Information Retrieval." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 1847–55. http://dx.doi.org/10.5373/jardcs/v12sp7/20202297.
Повний текст джерелаAziz, Noor Azizah Bt. "Choosing Appropriate Retrieval based Learning Elements among Students in Java Programming Course." International Journal of Psychosocial Rehabilitation 24, no. 5 (April 20, 2020): 5448–55. http://dx.doi.org/10.37200/ijpr/v24i5/pr2020251.
Повний текст джерелаRamirez-Arellano, Aldo, Juan Bory-Reyes, and Luis Manuel Hernández-Simón. "Learning Object Retrieval and Aggregation Based on Learning Styles." Journal of Educational Computing Research 55, no. 6 (December 6, 2016): 757–88. http://dx.doi.org/10.1177/0735633116681303.
Повний текст джерелаKarpicke, Jeffrey D., and Phillip J. Grimaldi. "Retrieval-Based Learning: A Perspective for Enhancing Meaningful Learning." Educational Psychology Review 24, no. 3 (August 4, 2012): 401–18. http://dx.doi.org/10.1007/s10648-012-9202-2.
Повний текст джерелаДисертації з теми "Retrieval-based learning"
Maleki-Dizaji, Saeedeh. "Evolutionary learning multi-agent based information retrieval systems." Thesis, Sheffield Hallam University, 2003. http://shura.shu.ac.uk/6856/.
Повний текст джерелаWu, Mengjiao. "Retrieval-based Metacognitive Monitoring in Self-regulated Learning." Kent State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=kent1532049448140424.
Повний текст джерелаChafik, Sanaa. "Machine learning techniques for content-based information retrieval." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLL008/document.
Повний текст джерелаThe amount of media data is growing at high speed with the fast growth of Internet and media resources. Performing an efficient similarity (nearest neighbor) search in such a large collection of data is a very challenging problem that the scientific community has been attempting to tackle. One of the most promising solutions to this fundamental problem is Content-Based Media Retrieval (CBMR) systems. The latter are search systems that perform the retrieval task in large media databases based on the content of the data. CBMR systems consist essentially of three major units, a Data Representation unit for feature representation learning, a Multidimensional Indexing unit for structuring the resulting feature space, and a Nearest Neighbor Search unit to perform efficient search. Media data (i.e. image, text, audio, video, etc.) can be represented by meaningful numeric information (i.e. multidimensional vector), called Feature Description, describing the overall content of the input data. The task of the second unit is to structure the resulting feature descriptor space into an index structure, where the third unit, effective nearest neighbor search, is performed.In this work, we address the problem of nearest neighbor search by proposing three Content-Based Media Retrieval approaches. Our three approaches are unsupervised, and thus can adapt to both labeled and unlabeled real-world datasets. They are based on a hashing indexing scheme to perform effective high dimensional nearest neighbor search. Unlike most recent existing hashing approaches, which favor indexing in Hamming space, our proposed methods provide index structures adapted to a real-space mapping. Although Hamming-based hashing methods achieve good accuracy-speed tradeoff, their accuracy drops owing to information loss during the binarization process. By contrast, real-space hashing approaches provide a more accurate approximation in the mapped real-space as they avoid the hard binary approximations.Our proposed approaches can be classified into shallow and deep approaches. In the former category, we propose two shallow hashing-based approaches namely, "Symmetries of the Cube Locality Sensitive Hashing" (SC-LSH) and "Cluster-based Data Oriented Hashing" (CDOH), based respectively on randomized-hashing and shallow learning-to-hash schemes. The SC-LSH method provides a solution to the space storage problem faced by most randomized-based hashing approaches. It consists of a semi-random scheme reducing partially the randomness effect of randomized hashing approaches, and thus the memory storage problem, while maintaining their efficiency in structuring heterogeneous spaces. The CDOH approach proposes to eliminate the randomness effect by combining machine learning techniques with the hashing concept. The CDOH outperforms the randomized hashing approaches in terms of computation time, memory space and search accuracy.The third approach is a deep learning-based hashing scheme, named "Unsupervised Deep Neuron-per-Neuron Hashing" (UDN2H). The UDN2H approach proposes to index individually the output of each neuron of the top layer of a deep unsupervised model, namely a Deep Autoencoder, with the aim of capturing the high level individual structure of each neuron output.Our three approaches, SC-LSH, CDOH and UDN2H, were proposed sequentially as the thesis was progressing, with an increasing level of complexity in terms of the developed models, and in terms of the effectiveness and the performances obtained on large real-world datasets
Govindarajan, Hariprasath. "Self-Supervised Representation Learning for Content Based Image Retrieval." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166223.
Повний текст джерелаAlzu’bi, Ahmad Gazi Suleiman. "Semantic content-based image retrieval using compact multifeatures and deep learning." Thesis, University of the West of Scotland, 2016. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.738480.
Повний текст джерелаcom, chungkp@yahoo, and Kien Ping Chung. "Intelligent content-based image retrieval framework based on semi-automated learning and historic profiles." Murdoch University, 2007. http://wwwlib.murdoch.edu.au/adt/browse/view/adt-MU20070831.123947.
Повний текст джерелаChung, Kien Ping. "Intelligent content-based image retrieval framework based on semi-automated learning and historic profiles." Thesis, Chung, Kien- Ping (2007) Intelligent content-based image retrieval framework based on semi-automated learning and historic profiles. PhD thesis, Murdoch University, 2007. https://researchrepository.murdoch.edu.au/id/eprint/666/.
Повний текст джерелаChung, Kien Ping. "Intelligent content-based image retrieval framework based on semi-automated learning and historic profiles." Chung, Kien- Ping (2007) Intelligent content-based image retrieval framework based on semi-automated learning and historic profiles. PhD thesis, Murdoch University, 2007. http://researchrepository.murdoch.edu.au/666/.
Повний текст джерелаWu, Zutao. "Kmer-based sequence representations for fast retrieval and comparison." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/103083/1/Zutao_Wu_Thesis.pdf.
Повний текст джерелаShevchuk, Danylo. "Audio Moment Retrieval based on Natural Language Query." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20094.
Повний текст джерелаКниги з теми "Retrieval-based learning"
Azuaje, Francisco Javier. An unsupervised neural learning approach to retrieval strategies for case-based reasoning and decision support. [s.l: The Author], 1999.
Знайти повний текст джерелаQing, Li, Klamma Ralf, Leung Howard, Specht Marcus, and SpringerLink (Online service), eds. Advances in Web-Based Learning - ICWL 2012: 11th International Conference, Sinaia, Romania, September 2-4, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
Знайти повний текст джерела1947-, Sharma S. K., ed. Creating knowledge based organizations. Hershey, PA: Idea Group Publishing, 2004.
Знайти повний текст джерелаDavid, Hutchison. Advances in Web Based Learning - ICWL 2008: 7th International Conference, Jinhua, China, August 20-22, 2008. Proceedings. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2008.
Знайти повний текст джерелаAdvances in multimedia and network information system technologies. Berlin: Springer, 2010.
Знайти повний текст джерелаThe evolution of inquiry: Controlled, guided, modeled, and free. Santa Barbara, California: Libraries Unlimited, an imprint of ABC-CLIO, LLC, 2015.
Знайти повний текст джерелаJackie, Carrigan, ed. Resource-based learning activities: Information literacy for high school students. Chicago, Ill: American Library Association, 1994.
Знайти повний текст джерелаInformation entrepreneurship: Information services based on the information lifecycle. Lanham, Md: Scarecrow Press, 2005.
Знайти повний текст джерелаWohlgenannt, Gerhard. Learning ontology relations by combining corpus-based techniques and reasoning on data from semantic web sources. Frankfurt am Main: P. Lang, 2011.
Знайти повний текст джерелаWohlgenannt, Gerhard. Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources. Bern: Peter Lang International Academic Publishers, 2018.
Знайти повний текст джерелаЧастини книг з теми "Retrieval-based learning"
Jing, Feng, Mingjing Li, Lei Zhang, Hong-Jiang Zhang, and Bo Zhang. "Learning in Region-Based Image Retrieval." In Lecture Notes in Computer Science, 206–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45113-7_21.
Повний текст джерелаBajwa, Manpreet Singh, Ravi Rana, and Geetanshi Bagga. "Machine Learning-Based Information Retrieval System." In Lecture Notes in Electrical Engineering, 13–22. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8297-4_2.
Повний текст джерелаGuo, Hui, Jie He, Caixu Xu, and Dongling Li. "Image Retrieval Algorithm Based on Fractal Coding." In Machine Learning and Intelligent Communications, 254–69. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04409-0_24.
Повний текст джерелаZhou, Zhi-Hua, Ke-Jia Chen, and Yuan Jiang. "Exploiting Unlabeled Data in Content-Based Image Retrieval." In Machine Learning: ECML 2004, 525–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30115-8_48.
Повний текст джерелаWu, Qihui, Rui Liu, Dongsheng Zhou, and Qiang Zhang. "3D Human Motion Retrieval Based on Graph Model." In E-Learning and Games, 219–27. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23712-7_29.
Повний текст джерелаThornley, Clare. "Teaching Information Retrieval Through Problem-Based Learning." In Teaching and Learning in Information Retrieval, 183–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22511-6_13.
Повний текст джерелаŚwieboda, Wojciech, Michał Meina, and Hung Son Nguyen. "Weight Learning in TRSM-based Information Retrieval." In Studies in Computational Intelligence, 61–74. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04714-0_5.
Повний текст джерелаFerguson, Valerie, Sheila Padden, Sigrid Rutishauser, and Michael Hollingsworth. "Information Retrieval Skills for Problem Based Learning." In Health Information Management: What Strategies?, 109–12. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-015-8786-0_34.
Повний текст джерелаPeng, Min, Jiajia Huang, Jiahui Zhu, Li Zhou, Hui Fu, Yanxiang He, and Fei Li. "Co-Learning Ranking for Query-Based Retrieval." In Lecture Notes in Computer Science, 468–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41230-1_39.
Повний текст джерелаHuiskes, Mark J. "Aspect-Based Relevance Learning for Image Retrieval." In Lecture Notes in Computer Science, 639–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11526346_67.
Повний текст джерелаТези доповідей конференцій з теми "Retrieval-based learning"
Zhang, Zhen-Hua, Yi-Nan Lu, Wen-Hui Li, and Gang Wang. "Segmentation-Based Image Retrieval." In 2007 International Conference on Machine Learning and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370428.
Повний текст джерелаXin Zhang, Bing Wang, Zhi-De Zhang, and Xiao-Yan Zhao. "SSVR-based image semantic retrieval." In 2008 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2008. http://dx.doi.org/10.1109/icmlc.2008.4620848.
Повний текст джерелаBiao Niu, Yifan Zhang, Jinqiao Wang, Jian Cheng, and Hanqing Lu. "Subspace learning based active learning for image retrieval." In 2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW). IEEE, 2013. http://dx.doi.org/10.1109/icmew.2013.6618268.
Повний текст джерелаWu, Chi-jiunn, Hui-chi Zeng, Szu-hao Huang, Shang-hong Lai, and Wen-hao Wang. "Learning-Based Interactive Video Retrieval System." In 2006 IEEE International Conference on Multimedia and Expo. IEEE, 2006. http://dx.doi.org/10.1109/icme.2006.262898.
Повний текст джерелаZhang, Zhen-hua, Yong Quan, Wen-hui Li, and Wu Guo. "A New Content-Based Image Retrieval." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.258801.
Повний текст джерелаOzyer, Gulsah Tumuklu, and Fatos Yarman Vural. "An Attention-Based Image Retrieval System." In 2011 Tenth International Conference on Machine Learning and Applications (ICMLA). IEEE, 2011. http://dx.doi.org/10.1109/icmla.2011.27.
Повний текст джерелаGilbert, Adam D., Ran Chang, and Xiaojun Qi. "A retrieval pattern-based inter-query learning approach for content-based image retrieval." In 2010 17th IEEE International Conference on Image Processing (ICIP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icip.2010.5654156.
Повний текст джерелаYu, Jerry. "Session details: Subspace learning in content-based image retrieval." In CIVR08: CIVR'08 - International Conference on Content-based Image and Video Retrieval. New York, NY, USA: ACM, 2008. http://dx.doi.org/10.1145/3247069.
Повний текст джерелаChang, Chun-guang, Ding-wei Wang, Ya-chen Liu, and Bao-ku Qi. "Fuzzy Similarity Measure Based Case Retrieval Method." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.258338.
Повний текст джерелаTanida, Jun, and Ryoichi Horisaki. "Learning-based signal retrieval from scattering media." In SPECKLE 2018: VII International Conference on Speckle Metrology, edited by Michal Józwik, Leszek R. Jaroszewicz, and Malgorzata Kujawińska. SPIE, 2018. http://dx.doi.org/10.1117/12.2322800.
Повний текст джерелаЗвіти організацій з теми "Retrieval-based learning"
Lee, Jung-Eun, Rong Jin, and Anil K. Jain. Ranked-Based Distance Metric Learning: An Application to Image Retrieval. Fort Belvoir, VA: Defense Technical Information Center, July 2008. http://dx.doi.org/10.21236/ada500953.
Повний текст джерелаLi, Eliot, Charles Nicholas, Tim Oates, and Raman K. Mehra. Intelligent Record Linkage Techniques Based on Information Retrieval, Natural Language Processing, and Machine Learning. Fort Belvoir, VA: Defense Technical Information Center, November 2002. http://dx.doi.org/10.21236/ada408937.
Повний текст джерелаKüsters, Ralf, and Ralf Molitor. Computing Least Common Subsumers in ALEN. Aachen University of Technology, 2000. http://dx.doi.org/10.25368/2022.110.
Повний текст джерелаKüsters, Ralf, and Ralf Molitor. Computing Least Common Subsumers in ALEN. Aachen University of Technology, 2000. http://dx.doi.org/10.25368/2022.110.
Повний текст джерела