Academic literature on the topic 'Transfer of Learning'

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Journal articles on the topic "Transfer of Learning"

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Alla, Sri Sai Meghana, and Kavitha Athota. "Brain Tumor Detection Using Transfer Learning in Deep Learning." Indian Journal Of Science And Technology 15, no. 40 (2022): 2093–102. http://dx.doi.org/10.17485/ijst/v15i40.1307.

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Würschinger, Hubert, Matthias Mühlbauer, and Nico Hanenkamp. "Transfer Learning für visuelle Kontrollaufgaben/Potentials of Transfer Learning." wt Werkstattstechnik online 110, no. 04 (2020): 264–69. http://dx.doi.org/10.37544/1436-4980-2020-04-98.

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In der industriellen Praxis wird eine Vielzahl von Prozess- und Qualitätskontrollaufgaben visuell von Mitarbeitern oder mithilfe von Kamerasystemen durchgeführt. Durch den Einsatz Künstlicher Intelligenz (KI) lässt sich die Programmierung und damit die Implementierung von Kamerasystemen effizienter gestalten. Im Bereich der Bildanalyse können dabei vortrainierte Künstliche Neuronale Netze verwendet werden. Das Anwenden dieser Netze auf neue Aufgaben wird dabei Transfer Learning genannt.   In industrial practice, a large number of process and quality control tasks are performed visuall
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Xu, Mingle, Sook Yoon, Jaesu Lee, and Dong Sun Park. "Unsupervised Transfer Learning for Plant Anomaly Recognition." Korean Institute of Smart Media 11, no. 4 (2022): 30–37. http://dx.doi.org/10.30693/smj.2022.11.4.30.

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Disease threatens plant growth and recognizing the type of disease is essential to making a remedy. In recent years, deep learning has witnessed a significant improvement for this task, however, a large volume of labeled images is one of the requirements to get decent performance. But annotated images are difficult and expensive to obtain in the agricultural field. Therefore, designing an efficient and effective strategy is one of the challenges in this area with few labeled data. Transfer learning, assuming taking knowledge from a source domain to a target domain, is borrowed to address this
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Tejaswini, Dubasi, and Uma Rani Vanamala. "Security System based on Transfer Learning Model." International Journal of Science and Research (IJSR) 12, no. 10 (2023): 1144–49. http://dx.doi.org/10.21275/sr231013184540.

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Cao, Bin, Sinno Jialin Pan, Yu Zhang, Dit-Yan Yeung, and Qiang Yang. "Adaptive Transfer Learning." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (2010): 407–12. http://dx.doi.org/10.1609/aaai.v24i1.7682.

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Transfer learning aims at reusing the knowledge in some source tasks to improve the learning of a target task. Many transfer learning methods assume that the source tasks and the target task be related, even though many tasks are not related in reality. However, when two tasks are unrelated, the knowledge extracted from a source task may not help, and even hurt, the performance of a target task. Thus, how to avoid negative transfer and then ensure a "safe transfer" of knowledge is crucial in transfer learning. In this paper, we propose an Adaptive Transfer learning algorithm based on Gaussian
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Yu, Zhengxu, Dong Shen, Zhongming Jin, Jianqiang Huang, Deng Cai, and Xian-Sheng Hua. "Progressive Transfer Learning." IEEE Transactions on Image Processing 31 (2022): 1340–48. http://dx.doi.org/10.1109/tip.2022.3141258.

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Renta-Davids, Ana-Inés, José-Miguel Jiménez-González, Manel Fandos-Garrido, and Ángel-Pío González-Soto. "Transfer of learning." European Journal of Training and Development 38, no. 8 (2014): 728–44. http://dx.doi.org/10.1108/ejtd-03-2014-0026.

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Purpose – This paper aims to analyse transfer of learning to workplace regarding to job-related training courses. Training courses analysed in this study are offered under the professional training for employment framework in Spain. Design/methodology/approach – During the training courses, trainees completed a self-reported survey of reasons for participation (time 1 data collection, N = 447). Two months after training, a second survey was sent to the trainees by email (time 2 data collection, N = 158). Factor analysis, correlations and multiple hierarchical regressions were performed. Findin
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Tetzlaff, Linda. "Transfer of learning." ACM SIGCHI Bulletin 17, SI (1986): 205–10. http://dx.doi.org/10.1145/30851.275631.

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Koçer, Barış, and Ahmet Arslan. "Genetic transfer learning." Expert Systems with Applications 37, no. 10 (2010): 6997–7002. http://dx.doi.org/10.1016/j.eswa.2010.03.019.

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Tetzlaff, Linda. "Transfer of learning." ACM SIGCHI Bulletin 18, no. 4 (1987): 205–10. http://dx.doi.org/10.1145/1165387.275631.

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Dissertations / Theses on the topic "Transfer of Learning"

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Shell, Jethro. "Fuzzy transfer learning." Thesis, De Montfort University, 2013. http://hdl.handle.net/2086/8842.

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The use of machine learning to predict output from data, using a model, is a well studied area. There are, however, a number of real-world applications that require a model to be produced but have little or no data available of the specific environment. These situations are prominent in Intelligent Environments (IEs). The sparsity of the data can be a result of the physical nature of the implementation, such as sensors placed into disaster recovery scenarios, or where the focus of the data acquisition is on very defined user groups, in the case of disabled individuals. Standard machine learnin
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Lu, Ying. "Transfer Learning for Image Classification." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEC045/document.

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Lors de l’apprentissage d’un modèle de classification pour un nouveau domaine cible avec seulement une petite quantité d’échantillons de formation, l’application des algorithmes d’apprentissage automatiques conduit généralement à des classifieurs surdimensionnés avec de mauvaises compétences de généralisation. D’autre part, recueillir un nombre suffisant d’échantillons de formation étiquetés manuellement peut s’avérer très coûteux. Les méthodes de transfert d’apprentissage visent à résoudre ce type de problèmes en transférant des connaissances provenant d’un domaine source associé qui contient
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Alexander, John W. "Transfer in reinforcement learning." Thesis, University of Aberdeen, 2015. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=227908.

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The problem of developing skill repertoires autonomously in robotics and artificial intelligence is becoming ever more pressing. Currently, the issues of how to apply prior knowledge to new situations and which knowledge to apply have not been sufficiently studied. We present a transfer setting where a reinforcement learning agent faces multiple problem solving tasks drawn from an unknown generative process, where each task has similar dynamics. The task dynamics are changed by varying in the transition function between states. The tasks are presented sequentially with the latest task presente
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Kiehl, Janet K. "Learning to Change: Organizational Learning and Knowledge Transfer." online version, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=case1080608710.

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Johnson, C. Dustin. "Set-Switching and Learning Transfer." Digital Archive @ GSU, 2008. http://digitalarchive.gsu.edu/psych_hontheses/7.

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In this experiment I investigated the relationship between set-switching and transfer learning, both of which presumably invoke executive functioning (EF), which may in turn be correlated with intelligence. Set-switching was measured by a computerized version of the Wisconsin Card Sort Task. Another computer task was written to measure learning-transfer ability. The data indicate little correlation between the ability to transfer learning and the capacity for set-switching. That is, these abilities may draw from independent cognitive mechanisms. The major difference may be requirement to
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Skolidis, Grigorios. "Transfer learning with Gaussian processes." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/6271.

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Transfer Learning is an emerging framework for learning from data that aims at intelligently transferring information between tasks. This is achieved by developing algorithms that can perform multiple tasks simultaneously, as well as translating previously acquired knowledge to novel learning problems. In this thesis, we investigate the application of Gaussian Processes to various forms of transfer learning with a focus on classification problems. This process initiates with a thorough introduction to the framework of Transfer learning, providing a clear taxonomy of the areas of research. Foll
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Chen, Xiaoyi. "Transfer Learning with Kernel Methods." Thesis, Troyes, 2018. http://www.theses.fr/2018TROY0005.

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Le transfert d‘apprentissage regroupe les méthodes permettant de transférer l’apprentissage réalisé sur des données (appelées Source) à des données nouvelles, différentes, mais liées aux données Source. Ces travaux sont une contribution au transfert d’apprentissage homogène (les domaines de représentation des Source et Cible sont identiques) et transductif (la tâche à effectuer sur les données Cible est identique à celle sur les données Source), lorsque nous ne disposons pas d’étiquettes des données Cible. Dans ces travaux, nous relâchons la contrainte d’égalité des lois des étiquettes conditi
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Al, Chalati Abdul Aziz, and Syed Asad Naveed. "Transfer Learning for Machine Diagnostics." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-43185.

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Fault detection and diagnostics are crucial tasks in condition-based maintenance. Industries nowadays are in need of fault identification in their machines as early as possible to save money and take precautionary measures in case of fault occurrence. Also, it is beneficial for the smooth interference in the manufacturing process in which it avoids sudden malfunctioning. Having sufficient training data for industrial machines is also a major challenge which is a prerequisite for deep neural networks to train an accurate prediction model. Transfer learning in such cases is beneficial as it can
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Arnekvist, Isac. "Transfer Learning using low-dimensional Representations in Reinforcement Learning." Licentiate thesis, KTH, Robotik, perception och lärande, RPL, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279120.

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Successful learning of behaviors in Reinforcement Learning (RL) are often learned tabula rasa, requiring many observations and interactions in the environment. Performing this outside of a simulator, in the real world, often becomes infeasible due to the large amount of interactions needed. This has motivated the use of Transfer Learning for Reinforcement Learning, where learning is accelerated by using experiences from previous learning in related tasks. In this thesis, I explore how we can transfer from a simple single-object pushing policy, to a wide array of non-prehensile rearrangement pr
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Mare, Angelique. "Motivators of learning and learning transfer in the workplace." Diss., University of Pretoria, 2015. http://hdl.handle.net/2263/52441.

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Motivating employees to learn and transfer their learning to their jobs is an important activity to ensure that employees - and the organisation - continuously adapt, evolve and survive in this highly turbulent environment. The literature shows that both intrinsic and extrinsic motivators influence learning and learning transfer, and the extent of influence could be different for different people. This research sets out to explore and identify the intrinsic and extrinsic motivational factors that drive learning and learning transfer. A qualitative study in the form of focus groups was conduct
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Books on the topic "Transfer of Learning"

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Hohensee, Charles, and Joanne Lobato, eds. Transfer of Learning. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65632-4.

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Hall, D. D. The transfer of learning. University of East Anglia, 1992.

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Razavi-Far, Roozbeh, Boyu Wang, Matthew E. Taylor, and Qiang Yang, eds. Federated and Transfer Learning. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-11748-0.

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Wang, Jindong. Introduction to transfer learning. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1109-7.

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Wehby, North Mary, ed. Successful transfer of learning. Krieger Pub. Co., 2011.

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Daffron, Sandra Ratcliff, and Sandra Ratcliff Daffron. Successful transfer of learning. Krieger Pub. Co., 2011.

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Wang, Jindong, and Yiqiang Chen. Introduction to Transfer Learning. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7584-4.

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Analoui, Farhad. Training and transfer of learning. Avebury, 1993.

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Gass, Susan M., and Larry Selinker, eds. Language Transfer in Language Learning. John Benjamins Publishing Company, 1992. http://dx.doi.org/10.1075/lald.5.

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Taylor, Matthew E. Transfer in Reinforcement Learning Domains. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01882-4.

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Book chapters on the topic "Transfer of Learning"

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Sarang, Poornachandra. "Transfer Learning." In Artificial Neural Networks with TensorFlow 2. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6150-7_4.

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Biermann, Henrik. "Transfer Learning." In Sportinformatik. Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-67026-2_23.

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Biermann, Henrik. "Transfer Learning." In Computer Science in Sport. Springer Berlin Heidelberg, 2024. http://dx.doi.org/10.1007/978-3-662-68313-2_23.

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Chin, Ting-Wu, and Cha Zhang. "Transfer Learning." In Computer Vision. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-03243-2_837-1.

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Amaratunga, Thimira. "Transfer Learning." In Deep Learning on Windows. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6431-7_7.

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Chin, Ting-Wu, and Cha Zhang. "Transfer Learning." In Computer Vision. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63416-2_837.

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Bhasin, Harsh. "Transfer Learning." In Hands-on Deep Learning. Apress, 2024. https://doi.org/10.1007/979-8-8688-1035-0_8.

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Yakubu Alhassan, Alhassan, and Alexander Ruser. "Knowledge Transfer." In Handbook Transdisciplinary Learning. transcript Verlag, 2023. http://dx.doi.org/10.14361/9783839463475-023.

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Rostami, Mohammad, Hangfeng He, Muhao Chen, and Dan Roth. "Transfer Learning via Representation Learning." In Federated and Transfer Learning. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11748-0_10.

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Utgoff, Paul E., James Cussens, Stefan Kramer, et al. "Inductive Transfer." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_401.

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Conference papers on the topic "Transfer of Learning"

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Guo, Weiyan, Xinru Zhang, Haowen Pang, and Chuyang Ye. "Transfer learning for brain lesion segmentation via data transfer." In 2024 International Conference on Future of Medicine and Biological Information Engineering (MBIE 2024), edited by Yudong Yao, Xiaoou Li, and Xia Yu. SPIE, 2024. http://dx.doi.org/10.1117/12.3048419.

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Arifuzzaman, Md, and Engin Arslan. "Learning Transfers via Transfer Learning." In 2021 IEEE Workshop on Innovating the Network for Data-Intensive Science (INDIS). IEEE, 2021. http://dx.doi.org/10.1109/indis54524.2021.00009.

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Chen, Guanliang, Dan Davis, Claudia Hauff, and Geert-Jan Houben. "Learning Transfer." In L@S 2016: Third (2016) ACM Conference on Learning @ Scale. ACM, 2016. http://dx.doi.org/10.1145/2876034.2876035.

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Muller, Brandon, Harith Al-Sahaf, Bing Xue, and Mengjie Zhang. "Transfer learning." In GECCO '19: Genetic and Evolutionary Computation Conference. ACM, 2019. http://dx.doi.org/10.1145/3319619.3322072.

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Liu, Tongliang, Qiang Yang, and Dacheng Tao. "Understanding How Feature Structure Transfers in Transfer Learning." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/329.

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Transfer learning transfers knowledge across domains to improve the learning performance. Since feature structures generally represent the common knowledge across different domains, they can be transferred successfully even though the labeling functions across domains differ arbitrarily. However, theoretical justification for this success has remained elusive. In this paper, motivated by self-taught learning, we regard a set of bases as a feature structure of a domain if the bases can (approximately) reconstruct any observation in this domain. We propose a general analysis scheme to theoretica
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Tetzlaff, Linda. "Transfer of learning." In the SIGCHI/GI conference. ACM Press, 1987. http://dx.doi.org/10.1145/29933.275631.

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Zhuang, Fuzhen, Ping Luo, Changying Du, Qing He, and Zhongzhi Shi. "Triplex transfer learning." In the sixth ACM international conference. ACM Press, 2013. http://dx.doi.org/10.1145/2433396.2433449.

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Zhu, Zhenfeng, Xingquan Zhu, Yangdong Ye, Yue-Fei Guo, and Xiangyang Xue. "Transfer active learning." In the 20th ACM international conference. ACM Press, 2011. http://dx.doi.org/10.1145/2063576.2063918.

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Long, Mingsheng, Jianmin Wang, Guiguang Ding, Wei Cheng, Xiang Zhang, and Wei Wang. "Dual Transfer Learning." In Proceedings of the 2012 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2012. http://dx.doi.org/10.1137/1.9781611972825.47.

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Cheng, Zhi-Qi, Xiao Wu, Siyu Huang, Jun-Xiu Li, Alexander G. Hauptmann, and Qiang Peng. "Learning to Transfer." In MM '18: ACM Multimedia Conference. ACM, 2018. http://dx.doi.org/10.1145/3240508.3240518.

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Reports on the topic "Transfer of Learning"

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Lozano-Perez, Tomas, and Leslie Kaelbling. Effective Bayesian Transfer Learning. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada516458.

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Chen, Yan, Arnab Bhattacharya, Jing Li, and Draguna Vrabie. Optimal Control by Transfer-Learning. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1988297.

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Kumar, Sharad. Localizing Little Landmarks with Transfer Learning. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.6703.

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Klenk, Matthew, and Kenneth D. Forbus. Learning Domain Theories via Analogical Transfer. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada470404.

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Cohen, Paul, and Carole Beal. LGIST: Learning Generalized Image Schemas for Transfer. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada491488.

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Kong, Q., A. Price, and S. Myers. Preliminary Transfer Learning Results on Israel Data. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1860678.

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Shu, Mengying. Deep learning for image classification on very small datasets using transfer learning. Iowa State University, 2019. http://dx.doi.org/10.31274/cc-20240624-493.

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Gorski, Nicholas A., and John E. Laird. Investigating Transfer Learning in the Urban Combat Testbed. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada478847.

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V. Nguyen, Cuong, and Cuong D. Do. Transfer Learning in ECG Diagnosis: Is It Effective? ResearchHub Technologies, Inc., 2024. https://doi.org/10.55277/researchhub.0t7mc9m1.

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V. Nguyen, Cuong, and Cuong D. Do. Transfer Learning in ECG Diagnosis: Is It Effective? ResearchHub Technologies, Inc., 2025. https://doi.org/10.55277/researchhub.0t7mc9m1.1.

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