Literatura académica sobre el tema "TRANSFER LEARNING APPROACH"
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Artículos de revistas sobre el tema "TRANSFER LEARNING APPROACH"
Durgut, Rafet, Mehmet Emin Aydin y Abdur Rakib. "Transfer Learning for Operator Selection: A Reinforcement Learning Approach". Algorithms 15, n.º 1 (17 de enero de 2022): 24. http://dx.doi.org/10.3390/a15010024.
Texto completoZhao, Peng, Guoqin Wu, Sheng Yao y HuiTing Liu. "A Transductive Transfer Learning Approach Based on Manifold Learning". Computing in Science & Engineering 22, n.º 1 (1 de enero de 2020): 77–87. http://dx.doi.org/10.1109/mcse.2018.2882699.
Texto completoMishra, Bishwas y Abhishek Samanta. "Quantum Transfer Learning Approach for Deepfake Detection". Sparklinglight Transactions on Artificial Intelligence and Quantum Computing 02, n.º 01 (2022): 17–27. http://dx.doi.org/10.55011/staiqc.2022.2103.
Texto completoHuang, Shuai, Jing Li, Kewei Chen, Teresa Wu, Jieping Ye, Xia Wu y Li Yao. "A transfer learning approach for network modeling". IIE Transactions 44, n.º 11 (2 de enero de 2012): 915–31. http://dx.doi.org/10.1080/0740817x.2011.649390.
Texto completoRaza, Noman, Asma Naseer, Maria Tamoor y Kashif Zafar. "Alzheimer Disease Classification through Transfer Learning Approach". Diagnostics 13, n.º 4 (20 de febrero de 2023): 801. http://dx.doi.org/10.3390/diagnostics13040801.
Texto completoCao, Bin, Sinno Jialin Pan, Yu Zhang, Dit-Yan Yeung y Qiang Yang. "Adaptive Transfer Learning". Proceedings of the AAAI Conference on Artificial Intelligence 24, n.º 1 (3 de julio de 2010): 407–12. http://dx.doi.org/10.1609/aaai.v24i1.7682.
Texto completoRani, Rajneesh y Harpreet Singh. "Fingerprint Presentation Attack Detection Using Transfer Learning Approach". International Journal of Intelligent Information Technologies 17, n.º 1 (enero de 2021): 53–67. http://dx.doi.org/10.4018/ijiit.2021010104.
Texto completoAswathi, T., T. R. Swapna y S. Padmavathi. "Transfer Learning approach for grading of Diabetic Retinopathy". Journal of Physics: Conference Series 1767, n.º 1 (1 de febrero de 2021): 012033. http://dx.doi.org/10.1088/1742-6596/1767/1/012033.
Texto completoOh, YongKyung, Namu Kim y Sungil Kim. "Transfer Learning based Approach for Mixture Gas Classification". Journal of the Korean Institute of Industrial Engineers 47, n.º 2 (30 de abril de 2021): 144–59. http://dx.doi.org/10.7232/jkiie.2021.47.2.144.
Texto completoCvetkovic, Stevica, Nemanja Savic y Ivan Ciric. "Deep Transfer Learning Approach for Robust Hand Detection". Intelligent Automation & Soft Computing 36, n.º 1 (2023): 967–79. http://dx.doi.org/10.32604/iasc.2023.032526.
Texto completoTesis sobre el tema "TRANSFER LEARNING APPROACH"
Andersen, Linda y Philip Andersson. "Deep Learning Approach for Diabetic Retinopathy Grading with Transfer Learning". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279981.
Texto completoDiabetisk näthinnesjukdom (DR) är en komplikation av diabetes och är en sjukdom som påverkar ögonen. Det är en av de största orsakerna till blindhet i västvärlden. Allt eftersom antalet människor med diabetes ökar, ökar även antalet med diabetisk näthinnesjukdom. Detta ställer högre krav på att bättre och effektivare resurser utvecklas för att kunna upptäcka sjukdomen i ett tidigt stadie, vilket är en förutsättning för att förhindra vidareutveckling av sjukdomen som i slutändan kan resultera i blindhet, och att vidare behandling av sjukdomen effektiviseras. Här spelar datorstödd diagnostik en viktig roll. Syftet med denna studie är att undersöka hur ett faltningsnätverk, tillsammans med överföringsinformation, kan prestera när det tränas för multiklass gradering av diabetisk näthinnesjukdom. För att göra detta användes ett färdigbyggt och färdigtränat faltningsnätverk, byggt i Keras, för att fortsättningsvis tränas och finjusteras i Tensorflow på ett 5-klassigt DR dataset. Totalt tjugo träningssessioner genomfördes och noggrannhet, sensitivitet och specificitet utvärderades i varje sådan session. Resultat visar att de uppnådda noggranheterna låg inom intervallet 35% till 48.5%. Den genomsnittliga testsensitiviteten för klass 0, 1, 2, 3 och 4 var 59.7%, 0.0%, 51.0%, 38.7% respektive 0.8%. Vidare uppnåddes en genomsnittlig testspecificitet för klass 1, 2, 3 och 4 på 77.8%, 100.0%, 62.4%, 80.2% respektive 99.7%. Den genomsnittliga sensitiviteten på 0.0% samt den genomsnittliga specificiteten på 100.0% för klass 1 (mild DR) erhölls eftersom CNN modellen aldrig förutsåg denna klass.
Xue, Yongjian. "Dynamic Transfer Learning for One-class Classification : a Multi-task Learning Approach". Thesis, Troyes, 2018. http://www.theses.fr/2018TROY0006.
Texto completoThe aim of this thesis is to minimize the performance loss of a one-class detection system when it encounters a data distribution change. The idea is to use transfer learning approach to transfer learned information from related old task to the new one. According to the practical applications, we divide this transfer learning problem into two parts, one part is the transfer learning in homogenous space and the other part is in heterogeneous space. A multi-task learning model is proposed to solve the above problem; it uses one parameter to balance the amount of information brought by the old task versus the new task. This model is formalized so that it can be solved by classical one-class SVM except with a different kernel matrix. To select the control parameter, a kernel path solution method is proposed. It computes all the solutions along that introduced parameter and criteria are proposed to choose the corresponding optimal solution at given number of new samples. Experiments show that this model can give a smooth transition from the old detection system to the new one whenever it encounters a data distribution change. Moreover, as the proposed model can be solved by classical one-class SVM, online learning algorithms for one-class SVM are studied later in the purpose of getting a constant false alarm rate. It can be applied to the online learning of the proposed model directly
Severan, Debra Devillier. "A Qualitative Approach to Transfer of Training for Managers in Leadership Development". ScholarWorks, 2019. https://scholarworks.waldenu.edu/dissertations/7570.
Texto completoWu, Michael. "Transfer Learning Approach to Powder Bed Fusion Additive Manufacturing Defect Detection". DigitalCommons@CalPoly, 2021. https://digitalcommons.calpoly.edu/theses/2324.
Texto completoWęckowska, Dagmara Maria. "Learning the ropes of the commercialisation of academic research : a practice-based approach to learning in knowledge transfer offices". Thesis, University of Sussex, 2013. http://sro.sussex.ac.uk/id/eprint/45183/.
Texto completoAllworth, James William. "A Machine Learning Approach to Space Debris Characterisation and Classification using Ground Based Optical Observations". Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29185.
Texto completoLopez, Lira Arjona Alfonso. "Inter-firm knowledge transfer and experiential learning| A business sustainability approach on SME's absorptive capacity". Thesis, Instituto Tecnologico y de Estudios Superiores de Monterrey (Mexico), 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3570884.
Texto completoIn emerging economies, Small and Medium-Sized Enterprises (SMEs) are threatened by continuous political and economic changes. In such uncertain environments, knowledge is the distinctive factor for the achievement of a competitive advantage. However, limited funds and pressure from competitors force SMEs to seek for external sources of knowledge.
The Multinational Corporation (MNC) represents an alternative for business sustainability within the value chain, including both suppliers and clients. In the aim for pursuing such endeavor, a conceptual framework including inter-firm knowledge transfer processes from the MNC and experiential learning enhanced by the Academia is explored.
In sum, this dissertation is intended to examine the MNC’s and Academia’s role on the procurement of SMEs’ business sustainability through inter-firm knowledge transfer and experiential learning, in terms of absorptive capacity. More specifically, the impact of technical and technological knowledge transferred from the MNC on one side; and reflective learning on managerial skills and business vision from the Academia on the other side, is analyzed through SMEs’ absorptive capacity. Regarding business sustainability, the effect of the application of newly absorbed knowledge is analyzed in terms of SMEs’ selected indicators for business improvements. As a complement, a qualitative study is included in order to provide support for findings hereby obtained.
Söderdahl, Fabian. "A Cross-Validation Approach to Knowledge Transfer for SVM Models in the Learning Using Privileged Information Paradigm". Thesis, Uppsala universitet, Statistiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385378.
Texto completoKraft, Erin. "Planning, Promoting and Assessing Social Learning in Sport: A Landscapes of Practice Approach". Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42009.
Texto completoCraig, Malcolm. "Factors that influence the receptivity to fault diagnostic learning when a systems approach is applied : a technical transfer study". Thesis, Cranfield University, 1992. http://hdl.handle.net/1826/4153.
Texto completoLibros sobre el tema "TRANSFER LEARNING APPROACH"
Hunt, John P. Strategic processing underlying transfer of learning: a modelling approach. Eugene: Microform Publications, College of Human development and performance, University of Oregon, 1989.
Buscar texto completoNorthern Ireland Credit Accumulation and Transfer System (Project). Designing learning programmes: A credit-based approach : a practical manual. Belfast: NICATS, 2002.
Buscar texto completoCollins, Gregg. Learning strategic concepts in competitive planning: An explanation-based approach to the transfer of knowledge across domains. Urbana, IL: Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1988.
Buscar texto completoSchneider, Lidz Carol, ed. Dynamic assessment: An interactional approach to evaluating learning potential. New York: Guilford Press, 1987.
Buscar texto completoJarvis, Scott. Approaching language transfer through text classification: Explorations in the detection-based approach. Bristol: Multilingual Matters, 2012.
Buscar texto completoFernandez, Maria. Farmers leading change: A learning approach to involving smallholders in the revitalization of their production systems. Kampala, Uganda: NARO, 2002.
Buscar texto completoPeter, Lusembo, ed. Farmers leading change: A learning approach to involving smallholders in the revitalization of their production systems. Kampala, Uganda: NARO, 2002.
Buscar texto completoSuzanne, Jacob y Hébert Danièle, eds. Pour guider la métacognition. Sainte-Foy: Presses de l'Université du Québec, 2000.
Buscar texto completoFogarty, Robin. Patterns for thinking, patterns for transfer: A cooperative team approach for critical and creative thinking in the classroom. 2a ed. Palatine, Ill: IRI/SkyLight Educational Training and Publishing Inc., 1993.
Buscar texto completoFogarty, Robin. Patterns for thinking, patterns for transfer: A cooperative team approach for critical and creative thinking in the classroom. 4a ed. Palatine, Ill. (200 E. Wood St., Suite 250, Palatine 60067): IRI Group, 1989.
Buscar texto completoCapítulos de libros sobre el tema "TRANSFER LEARNING APPROACH"
Zhang, Linrui, Yisheng Zhou, Tatiana Erekhinskaya y Dan Moldovan. "Emoji Prediction: A Transfer Learning Approach". En Advances in Intelligent Systems and Computing, 864–72. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39442-4_65.
Texto completoKhandelwal, Shekhar y Rik Das. "Transfer Learning Approach in Phishing Detection". En Phishing Detection Using Content-Based Image Classification, 37–46. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003217381-4.
Texto completoBrown, Christopher J. y Diane Morrad. "SDL Approach to University-Small Business Learning: Mapping the Learning Journey". En Innovation through Knowledge Transfer 2012, 233–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34219-6_26.
Texto completoAgarwal, Nancy, Tuğçe Ünlü, Mudasir Ahmad Wani y Patrick Bours. "Predatory Conversation Detection Using Transfer Learning Approach". En Machine Learning, Optimization, and Data Science, 488–99. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95467-3_35.
Texto completoLi, Wei, Shuai Ding, Yi Chen y Shanlin Yang. "A Transfer Learning Approach for Credit Scoring". En Advances in Intelligent Systems and Computing, 64–73. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98776-7_8.
Texto completoKajdanowicz, Tomasz, Slawomir Plamowski, Przemyslaw Kazienko y Wojciech Indyk. "Transfer Learning Approach to Debt Portfolio Appraisal". En Lecture Notes in Computer Science, 46–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28931-6_5.
Texto completoAbou Baker, Nermeen, Jonas Stehr y Uwe Handmann. "Transfer Learning Approach Towards a Smarter Recycling". En Lecture Notes in Computer Science, 685–96. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15919-0_57.
Texto completoVries, Marc J. de. "Transfer in Technology Through a Concept-Context Approach". En Transfer, Transitions and Transformations of Learning, 13–22. Rotterdam: SensePublishers, 2013. http://dx.doi.org/10.1007/978-94-6209-437-6_2.
Texto completoKrishnamoorthy, Sujatha. "Transfer Learning Architecture Approach for Smart Transportation System". En Communications in Computer and Information Science, 162–81. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09469-9_15.
Texto completoYenneti, Shanmukha Sai Sumanth, Riti Kushwaha, Smita Naval y Gaurav Singal. "Leading Athlete Following UAV Using Transfer Learning Approach". En Communications in Computer and Information Science, 424–33. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0401-0_33.
Texto completoActas de conferencias sobre el tema "TRANSFER LEARNING APPROACH"
Kicki, Piotr y Krzysztof Walas. "Friction from Reflectance: Transfer Learning Approach". En 2019 4th International Conference on Robotics and Automation Engineering (ICRAE). IEEE, 2019. http://dx.doi.org/10.1109/icrae48301.2019.9043793.
Texto completoWitherow, Megan, Manar D. Samad y Khan M. Iftekharuddin. "Transfer learning approach to multiclass classification of child facial expressions". En Applications of Machine Learning, editado por Michael E. Zelinski, Tarek M. Taha, Jonathan Howe, Abdul A. Awwal y Khan M. Iftekharuddin. SPIE, 2019. http://dx.doi.org/10.1117/12.2530397.
Texto completoKatranji, Mehdi, Etienne Thuillier, Sami Kraiem, Laurent Moalic y Fouad Hadj Selem. "Mobility data disaggregation: A transfer learning approach". En 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2016. http://dx.doi.org/10.1109/itsc.2016.7795783.
Texto completoUpadhyaya, Prashant, Ruchi y Suniti Dutt. "Transfer Learning Approach for 6G-IoT Applications". En 2022 7th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2022. http://dx.doi.org/10.1109/icces54183.2022.9835931.
Texto completoSakunrasrisuay, Chinapat, Pakarat Musikawan, Anh-Nhat Nguyen, Yanika Kongsorot, Phet Aimtongkham y Chakchai So-In. "Tomato Maturity Classification: A Transfer Learning Approach". En 2021 25th International Computer Science and Engineering Conference (ICSEC). IEEE, 2021. http://dx.doi.org/10.1109/icsec53205.2021.9684584.
Texto completoSingh, Richa, Ashwani Kumar Dubey y Rajiv Kapoor. "Denoised Autoencoder using DCNN Transfer Learning Approach". En 2022 International Mobile and Embedded Technology Conference (MECON). IEEE, 2022. http://dx.doi.org/10.1109/mecon53876.2022.9751863.
Texto completoSancinetti, Marcelo, Jazmin Vidal, Cyntia Bonomi y Luciana Ferrer. "A Transfer Learning Approach for Pronunciation Scoring". En ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022. http://dx.doi.org/10.1109/icassp43922.2022.9747727.
Texto completoKoupilová, Zdeňka. "TIPS for active learning approach in distance learning conditions". En DIDACTIC TRANSFER OF PHYSICS KNOWLEDGE THROUGH DISTANCE EDUCATION: DIDFYZ 2021. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0078620.
Texto completoFarid, Dewan, Aicha Sekhari y Ouzrout Yacine. "CLUSTER-BASED KNOWLEDGE TRANSFER APPROACH FOR SMART FARMING". En 12th International Conference on Education and New Learning Technologies. IATED, 2020. http://dx.doi.org/10.21125/edulearn.2020.1867.
Texto completoWu, Peilun, Hui Guo y Richard Buckland. "A Transfer Learning Approach for Network Intrusion Detection". En 2019 IEEE 4th International Conference on Big Data Analytics (ICBDA). IEEE, 2019. http://dx.doi.org/10.1109/icbda.2019.8713213.
Texto completoInformes sobre el tema "TRANSFER LEARNING APPROACH"
Roschelle, Jeremy, Britte Haugan Cheng, Nicola Hodkowski, Lina Haldar y Julie Neisler. Transfer for Future Learning of Fractions within Cignition’s Microtutoring Approach. Digital Promise, abril de 2020. http://dx.doi.org/10.51388/20.500.12265/95.
Texto completoLintern, G. A Perceptual Learning Approach to Skill Transfer for Manual Control. Fort Belvoir, VA: Defense Technical Information Center, enero de 1985. http://dx.doi.org/10.21236/ada154964.
Texto completoLytvynova, Svitlana, Oleksandr Burov, Nataliia Demeshkant, Viacheslav Osadchyi, Сергій Олексійович Семеріков, Світлана Григорівна Литвинова, Олександр Юрійович Буров, Наталія Андріївна Демешкант y В'ячеслав Володимирович Осадчий. Proceedings of the VI International Workshop on Professional Retraining and Life-Long Learning using ICT: Person-oriented Approach (3L-Person 2021) co-located with 17th International Conference on ICT in Education, Research, and Industrial Applications: Integration, Harmonization, and Knowledge Transfer (ICTERI 2021), Kherson, Ukraine, October 1, 2021. Криворізький державний педагогічний університет, marzo de 2022. http://dx.doi.org/10.31812/123456789/6988.
Texto completoBrizard, Jean-Claude. Breaking With the Past: Embracing Digital Transformation in Education. Digital Promise, abril de 2023. http://dx.doi.org/10.51388/20.500.12265/176.
Texto completoGonzález, Javier, Dante Castillo-Canales, Monserrat Creamer y Magali Ramos Jarrin. Misalignments and Incoherencies within Ecuador's Education System: How Well Are Key Actors and Public Efforts Aligned towards Better Learning Outcomes? Research on Improving Systems of Education (RISE), marzo de 2023. http://dx.doi.org/10.35489/bsg-rise-wp_2023/137.
Texto completoAbdula, Andrii I., Halyna A. Baluta, Nadiia P. Kozachenko y Darja A. Kassim. Peculiarities of using of the Moodle test tools in philosophy teaching. [б. в.], julio de 2020. http://dx.doi.org/10.31812/123456789/3867.
Texto completoPrisacariu, Roxana. Swiss immigrants’ integration policy as inspiration for the Romanian Roma inclusion strategy. Fribourg (Switzerland): IFF, 2015. http://dx.doi.org/10.51363/unifr.diff.2015.05.
Texto completoRuvinsky, Alicia, Timothy Garton, Daniel Chausse, Rajeev Agrawal, Harland Yu y Ernest Miller. Accelerating the tactical decision process with High-Performance Computing (HPC) on the edge : motivation, framework, and use cases. Engineer Research and Development Center (U.S.), septiembre de 2021. http://dx.doi.org/10.21079/11681/42169.
Texto completoKharkivska, Alla A., Liudmyla V. Shtefan, Muntasir Alsadoon y Aleksandr D. Uchitel. Technology of forming future journalists' social information competence in Iraq based on the use of a dynamic pedagogical site. [б. в.], julio de 2020. http://dx.doi.org/10.31812/123456789/3853.
Texto completoBaliki, Ghassan, Dorothee Weiffen, Melodie Al Daccache, Aysegül Kayaoglu, Lara Sujud, Hadi Jaafar, Hala Ghattas y Tilman Brück. Seeds for recovery: The long-term impacts of a complex agricultural intervention on welfare, behaviour and stability in Syria (SEEDS). Centre for Excellence and Development Impact and Learning (CEDIL), abril de 2023. http://dx.doi.org/10.51744/crpp7.
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