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Relevant bibliographies by topics / Online and Offline Learning

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Dissertations / Theses
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Academic literature on the topic 'Online and Offline Learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 February 2022

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Journal articles on the topic "Online and Offline Learning"

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Opper, Manfred. "Online versus offline learning." Philosophical Magazine B 77, no. 5 (1998): 1531–37. http://dx.doi.org/10.1080/13642819808205045.

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Son, Jeong-Bae. "Reading Online and Offline." International Journal of Computer-Assisted Language Learning and Teaching 3, no. 4 (2013): 25–32. http://dx.doi.org/10.4018/ijcallt.2013100103.

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Reading online is different from reading offline. Are language teachers’ strategies for reading electronic texts and reading printed texts different? Do language teachers have different attitudes toward reading printed texts and reading electronic texts, particularly web-based reading materials? Several groups of in-service language teachers enrolled in a postgraduate course on computer-assisted language learning offered by an Australian university were asked to respond to these questions in online discussion forums. This article presents data from the discussion forums and investigates the te

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Ewing, Jim. "Enhancement of Online and Offline Student Learning." Educational Media International 37, no. 4 (2000): 205–17. http://dx.doi.org/10.1080/09523980050210394.

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Ai, Qingyao, Tao Yang, Huazheng Wang, and Jiaxin Mao. "Unbiased Learning to Rank." ACM Transactions on Information Systems 39, no. 2 (2021): 1–29. http://dx.doi.org/10.1145/3439861.

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How to obtain an unbiased ranking model by learning to rank with biased user feedback is an important research question for IR. Existing work on unbiased learning to rank (ULTR) can be broadly categorized into two groups—the studies on unbiased learning algorithms with logged data, namely, the offline unbiased learning, and the studies on unbiased parameters estimation with real-time user interactions, namely, the online learning to rank. While their definitions of unbiasness are different, these two types of ULTR algorithms share the same goal—to find the best models that rank documents based

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Eryani, Y., and B. Mulyanti. "Technology-based blended learning to accommodate offline and online learning." IOP Conference Series: Materials Science and Engineering 1098, no. 3 (2021): 032010. http://dx.doi.org/10.1088/1757-899x/1098/3/032010.

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Shen, Aihong, Shengjing Tian, Guoqiang Tian, Jie Zhang, and Xiuping Liu. "Visual Tracking Jointly With Online and Offline Learning." IEEE Access 8 (2020): 181091–101. http://dx.doi.org/10.1109/access.2020.3028308.

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Srivastava, Ritesh, and M. P. S. Bhatia. "Offline vs. Online Sentiment Analysis." International Journal of Information Retrieval Research 7, no. 4 (2017): 1–18. http://dx.doi.org/10.4018/ijirr.2017100101.

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Recently, the social networking sites (SNSs) have proven their immense power of prediction for predicting the results of the real-world events. However, for real-time monitoring of the world activities via microblogging site like Twitter, it is important to perform the sentiment analysis of online micro-texts in real-time to support fast and intelligent decision-making and hence to execute the appropriate actions in the real world in real-time. In this context, this paper discusses the online sentiment analysis process of online micro-texts in perspectives of the real-time analysis process. In

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Pratama, Rio Erwan, Sri Mulyati, and Iwan Susanto. "Pembelajaran Daring dan Luring pada Masa Pandemi Covid-19." Social, Humanities, and Educational Studies (SHEs): Conference Series 3, no. 4 (2021): 354. http://dx.doi.org/10.20961/shes.v3i4.53368.

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<p><em>This study aims to explore whether online learning and offline learning can work well, so that educational goals can be achieved. This research is a phenomenological qualitative research to find out how the application of online learning and offline learning in one of the SD Negeri Sepatnunggal 02 in Cilacap Regency, Central Java, Indonesia. The steps of this research include action planning, action implementation, observation stage, and reflection stage. From the results of the study, there were several obstacles in its implementation, but they could be solved well by the t

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RACHMAH, NURUL. "Effectiveness of Online vs Offline classes for EFL Classroom : a study case in a higher education." Journal of English Teaching, Applied Linguistics and Literatures (JETALL) 3, no. 1 (2020): 19. http://dx.doi.org/10.20527/jetall.v3i1.7703.

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With the increasing development of technology, online teaching is more readily accepted as a viable component in teaching and learning. Online and Offline class, particularly in developing countries, in its early stages and not without its challenges. This study exposed 16 EFL students of university Ibnu Khaldun responses to an online classroom and offline classroom. And investigates common student perceptions of the online lesson as compared with offline lessons to identify the effectivness between online and offline class. The method use data analysis adopted from questionnaires using qualit

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Sollich, Peter, and David Barber. "Online Learning from Finite Training Sets and Robustness to Input Bias." Neural Computation 10, no. 8 (1998): 2201–17. http://dx.doi.org/10.1162/089976698300017034.

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We analyze online gradient descent learning from finite training sets at noninfinitesimal learning rates η. Exact results are obtained for the time-dependent generalization error of a simple model system: a linear network with a large number of weights N, trained on p = αN examples. This allows us to study in detail the effects of finite training set size α on, for example, the optimal choice of learning rate η. We also compare online and offline learning, for respective optimal settings of η at given final learning time. Online learning turns out to be much more robust to input bias and actua

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Dissertations / Theses on the topic "Online and Offline Learning"

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Wang, Li Ph D. Massachusetts Institute of Technology. "Online and offline learning in operations." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/129080.

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Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, September, 2020<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 213-219).<br>With the rapid advancement of information technology and accelerated development of data science, the importance of integrating data into decision-making has never been stronger. In this thesis, we propose data-driven algorithms to incorporate learning from data in three operations problems, concerning both online learning and offline learning settings. First, we study a

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Woodley, Thomas Edward. "Visual tracking using offline and online learning." Thesis, University of Cambridge, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.608814.

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Minerva, Michela. "Automated Configuration of Offline/Online Algorithms: an Empirical Model Learning Approach." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22649/.

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The energy management system is the intelligent core of a virtual power plant and it manages power flows among units in the grid. This implies dealing with optimization under uncertainty because entities such as loads and renewable energy resources have stochastic behaviors. A hybrid offline/online optimization technique can be applied in such problems to ensure efficient online computation. This work devises an approach that integrates machine learning and optimization models to perform automatic algorithm configuration. It is inserted as the top component in a two-level hierarchical optimiz

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Dieckmann, Andreas. "Generische E-Learning-Plattform für interaktive Lehrsimulationen zum Einsatz in Selbststudium und Präsenzlehre online und offline." [S.l. : s.n.], 2003. http://deposit.ddb.de/cgi-bin/dokserv?idn=971725322.

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Corneliussen, Snorre Christoffer, and Magnus Westergaard. "Combining offline and online learning in developing an adaptive controller for a simulated car racing environment." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-13640.

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This report presents the work done to develop an autonomous driverfor the Simulated Car Racing Championship (SCRC), a competition incomputational intelligence based on The Open Racing Car Simulator(TORCS). Autonomous race driving based only on local sensory datais a complex problem, and previous SCRC entries' work show a widevariety of approaches taken to address it. We describe CRABCAR,a controller that combines oine learning prior to the competitionwith online learning during the competition to optimize its performance in the SCRC context. The presented approach extends and builds on tr

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Arnell, Matilda, and Yuliya Bilinskaya. "Business opportunity creation through Social Networking Sites : A network perspective." Thesis, Uppsala universitet, Företagsekonomiska institutionen, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-179833.

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Bövik, Therese, and Lisa Pålsson. "Online going offline : Why online fashion retailers expand through an offline strategy." Thesis, Högskolan i Borås, Akademin för textil, teknik och ekonomi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-701.

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During the previous years there has been a lot of focus on e-commerce in the fashion and clothing industry and that everything will be bought online in the future. However, several e-commerce companies have decided to expand into offline retail. This dissimilarity between theory and reality creates an interest for further research and a curiosity of how the future within retail will develop. The purpose of this research is to understand why Swedish online fashion retailers expand through an offline strategy. An Expansion Theme Model, which emerged from the theoretical framework, is used throug

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Cuaranda, Gastón. "Marketing offline vs online." Bachelor's thesis, Universidad Nacional de Cuyo. Facultad de Ciencias Económicas, 2014. http://bdigital.uncu.edu.ar/6927.

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En este trabajo se analiza la sinergia actual entre marketing offline y online, y la evolución de dicha disciplina a lo largo de las últimas décadas. Además se pretende exponer un modelo de gestión de redes sociales, que sirva de base para futuras campañas integrales de marketing.<br>Fil: Cuaranda, Gastón. Universidad Nacional de Cuyo. Facultad de Ciencias Económicas.

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Asan, N. Evren. "Offline And Online Disk Scheduling Problems." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12607909/index.pdf.

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This thesis considers the disk scheduling problem. The problem is investigated in two types of settings: offline and online. We first adopt the traveling salesman problem with time windows in the scheduling literature for solving the offline problem. Then we develop a decision epoch scheme in which offline problems are iteratively used in solving the online problem. We perform an experimental study for our approach and two well-known disk scheduling algorithms, and compare them according to several performance criteria.

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Yu, Ping Tate Stephen R. "Direct online/offline digital signatures schemes." [Denton, Tex.] : University of North Texas, 2008. http://digital.library.unt.edu/permalink/meta-dc-9717.

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Books on the topic "Online and Offline Learning"

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Kreutzer, Ralf T. Kundendialog online und offline. Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-30119-4.

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Gentzkow, Matthew. Ideological segregation online and offline. National Bureau of Economic Research, 2010.

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Platt, Stephan. Von der Offline- zur Online-Kommunikation. Deutscher Universitätsverlag, 2005. http://dx.doi.org/10.1007/978-3-322-81195-0.

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Roberts, Mary Lou. Internet marketing: Integrating online and offline strategies. 2nd ed. Thomson, 2008.

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Lauer, Karin. Strategische Preisgestaltung in Offline- und Online-Vertriebskanälen. Springer Fachmedien Wiesbaden, 2018. http://dx.doi.org/10.1007/978-3-658-22494-3.

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L, Zahay Debra, ed. Internet marketing: Integrating online and offline strategies. 3rd ed. South-Western Cengage Learning, 2012.

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Horst, Bunke, ed. Recognition of whiteboard notes: Online, offline and combination. World Scientific, 2008.

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Liwicki, Marcus. Recognition of whiteboard notes: Online, offline and combination. World Scientific, 2008.

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Thomas, Michael. Online Learning. SAGE Publications Ltd, 2011. http://dx.doi.org/10.4135/9781446262719.

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Anderson, David. Learning online. Matrix, 1990.

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Book chapters on the topic "Online and Offline Learning"

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Ben-David, Shai, Eyal Kushilevitz, and Yishay Mansour. "Online learning versus offline learning." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-59119-2_167.

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Yan, Yuguang, Qingyao Wu, Mingkui Tan, and Huaqing Min. "Online Heterogeneous Transfer Learning by Weighted Offline and Online Classifiers." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49409-8_38.

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Atzmueller, Martin. "Analyzing and Grounding Social Interaction in Online and Offline Networks." In Machine Learning and Knowledge Discovery in Databases. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44845-8_41.

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Guo, Enhua, and Fuxin Zhang. "From Offline to Online: The Reduced Embodiment in Teacher-Initiated Turn-Taking in GSL Instructions." In Learning Technologies and Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66906-5_34.

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Nguyen, Duong D., Arvind Rajagopalan, and Cheng-Chew Lim. "Online Versus Offline Reinforcement Learning for False Target Control Against Known Threat." In Intelligent Robotics and Applications. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97589-4_34.

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Mukherjee, Himadri, Shibaprasad Sen, Ankita Dhar, Sk Md Obaidullah, and Kaushik Roy. "Deep Learning-Based Bangla Isolated Character Recognition from Online and Offline Data." In Communications in Computer and Information Science. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8697-2_46.

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Yang, Yu, Zhiyuan Wen, Jiannong Cao, Jiaxing Shen, Hongzhi Yin, and Xiaofang Zhou. "EPARS: Early Prediction of At-Risk Students with Online and Offline Learning Behaviors." In Database Systems for Advanced Applications. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59416-9_1.

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Zhang, Xue-ping, Fei-fei Qin, Ting Zhang, and Chang-min Lv. "Design of Collaborative Teaching Mode of Online and Offline Based on Supervised Learning Algorithm." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63952-5_21.

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Belaid, Lauren M. "Designing HAYA! ABC: Utilizing Transmedia Storytelling to Teach English to K-12 Online Arab ELLs—A Framework for Encouraging Online and Offline L2 Participation." In Expanding Global Horizons Through Technology Enhanced Language Learning. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-7579-2_8.

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Passot, Jean-Baptiste, Niceto Luque, and Angelo Arleo. "Internal Models in the Cerebellum: A Coupling Scheme for Online and Offline Learning in Procedural Tasks." In From Animals to Animats 11. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15193-4_41.

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Conference papers on the topic "Online and Offline Learning"

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Shimoyamada, Sho, Akiko Nakazawa, and Toru Fujimoto. "Blending Online and Offline Learning." In ICEIT 2019: 2019 8th International Conference on Educational and Information Technology. ACM, 2019. http://dx.doi.org/10.1145/3318396.3318405.

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Napier, Alyssa, Elizabeth Huttner-Loan, and Justin Reich. "From online learning to offline action." In L@S '18: Fifth (2018) ACM Conference on Learning @ Scale. ACM, 2018. http://dx.doi.org/10.1145/3231644.3231674.

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Yu, Mei, Tianyi Xu, Jie Gao, et al. "Research on Integrating Online Learning into Offline Teaching." In 2018 13th International Conference on Computer Science & Education (ICCSE). IEEE, 2018. http://dx.doi.org/10.1109/iccse.2018.8468738.

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Fujii, Yuto, and Yoji Kuroda. "Robust localization system using online / offline hybrid learning." In 2011 IEEE/SICE International Symposium on System Integration (SII 2011). IEEE, 2011. http://dx.doi.org/10.1109/sii.2011.6147636.

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Phong, Nguyen Huu, and Bernardete Ribeiro. "Offline and online deep learning for image recognition." In 2017 4th Experiment@International Conference (exp.at'17). IEEE, 2017. http://dx.doi.org/10.1109/expat.2017.7984421.

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Wei, Hua, Deheng Ye, Zhao Liu, et al. "Boosting Offline Reinforcement Learning with Residual Generative Modeling." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/492.

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Offline reinforcement learning (RL) tries to learn the near-optimal policy with recorded offline experience without online exploration.Current offline RL research includes: 1) generative modeling, i.e., approximating a policy using fixed data; and 2) learning the state-action value function. While most research focuses on the state-action function part through reducing the bootstrapping error in value function approximation induced by the distribution shift of training data, the effects of error propagation in generative modeling have been neglected. In this paper, we analyze the error in gene

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Fischer, Lydia, Barbara Hammer, and Heiko Wersing. "Combining offline and online classifiers for life-long learning." In 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. http://dx.doi.org/10.1109/ijcnn.2015.7280678.

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Zhang, Xiao Rui. "LEARNING ONLINE AND OFFLINE: HOW DO THEY GO TOGETHER?" In International Technology, Education and Development Conference. IATED, 2016. http://dx.doi.org/10.21125/inted.2016.2139.

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Hu, Mengjie, Zhenzhong Wei, and Guangjun Zhang. "Long-term object tracking combined offline with online learning." In International Symposium on Optoelectronic Technology and Application 2014, edited by Gaurav Sharma, Fugen Zhou, and Jennifer Liu. SPIE, 2014. http://dx.doi.org/10.1117/12.2073119.

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Ouyang, P. R., and Pong-in Pipatpaibul. "Iterative Learning Control: A Comparison Study." In ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-37161.

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Iterative Learning Control (ILC) is a technique of tracking control aiming at improving tracking performance for systems that work in a repetitive mode. ILC is a simple and effective control and can progressively reduce tracking errors and improve system performance from iteration to iteration. In this paper, we first classify the ILC schemes into three categories: offline learning scheme, online learning scheme, and online-offline learning scheme. In each scheme, P-type, D-type, PD-type, and switching gain learning control are discussed. The corresponding convergence conditions for each type

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Reports on the topic "Online and Offline Learning"

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Gentzkow, Matthew, and Jesse Shapiro. Ideological Segregation Online and Offline. National Bureau of Economic Research, 2010. http://dx.doi.org/10.3386/w15916.

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Areti, H. Future microprocessor farms: Offline and online. Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/7020075.

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Bitter, Olivia Meredith, and Wesley Ketchum. Online and Offline Monitoring of the ICARUS Detector. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1599312.

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Ellison, Glenn, and Sara Fisher Ellison. Internet Retail Demand: Taxes, Geography, and Online-Offline Competition. National Bureau of Economic Research, 2006. http://dx.doi.org/10.3386/w12242.

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Gil-Clavel, Beatriz Sofía, Emilio Zagheni, and Valeria Bordone. Close social networks among older adults: the online and offline perspectives. Max Planck Institute for Demographic Research, 2020. http://dx.doi.org/10.4054/mpidr-wp-2020-035.

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Cavallo, Alberto. Are Online and Offline Prices Similar? Evidence from Large Multi-Channel Retailers. National Bureau of Economic Research, 2016. http://dx.doi.org/10.3386/w22142.

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Wu, D. Derek. Online Learning in Postsecondary Education. Ithaka S+R, 2015. http://dx.doi.org/10.18665/sr.221027.

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Chingos, Matthew, Christine Mulhern, Rebecca Griffiths, and Richard Spies. Interactive Online Learning on Campus. Ithaka S+R, 2015. http://dx.doi.org/10.18665/sr.22522.

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Scrivens, Ryan, Steven M. Chermak, Joshua D. Freilich, Thomas W. Wojciechowski, and Richard Frank. Detecting Extremists Online: Examining Online Posting Behaviors of Violent and Non-Violent Right-Wing Extremists. RESOLVE Network, 2021. http://dx.doi.org/10.37805/pn2021.21.remve.

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Like most of us, violent extremists often leave a digital footprint behind. Researchers, practitioners, and policymakers raise questions about whether violent individuals can be identified online prior to their attacks offline based on their online posting behaviors. Despite ongoing concerns, few empirically grounded analyses have identified which online users have engaged in violent extremism offline and then assessed their digital footprints, and fewer analyses have identified differences in posting behaviors of those who share extreme ideological beliefs but are violent or non-violent in th

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Freeman, Richard, and M. Marit Rehavi. Helping Workers Online and Offline: Innovations in Union and Worker Organization Using the Internet. National Bureau of Economic Research, 2008. http://dx.doi.org/10.3386/w13850.

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