Literatura académica sobre el tema "Concept-Drift Management"
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Artículos de revistas sobre el tema "Concept-Drift Management"
Cano, Andrés, Manuel Gómez-Olmedo y Serafín Moral. "A Bayesian approach to abrupt concept drift". Knowledge-Based Systems 185 (diciembre de 2019): 104909. http://dx.doi.org/10.1016/j.knosys.2019.104909.
Texto completoBayram, Firas, Bestoun S. Ahmed y Andreas Kassler. "From concept drift to model degradation: An overview on performance-aware drift detectors". Knowledge-Based Systems 245 (junio de 2022): 108632. http://dx.doi.org/10.1016/j.knosys.2022.108632.
Texto completoElkhawaga, Ghada, Mervat Abuelkheir, Sherif I. Barakat, Alaa M. Riad y Manfred Reichert. "CONDA-PM—A Systematic Review and Framework for Concept Drift Analysis in Process Mining". Algorithms 13, n.º 7 (3 de julio de 2020): 161. http://dx.doi.org/10.3390/a13070161.
Texto completoZheng, Xiulin, Peipei Li, Xuegang Hu y Kui Yu. "Semi-supervised classification on data streams with recurring concept drift and concept evolution". Knowledge-Based Systems 215 (marzo de 2021): 106749. http://dx.doi.org/10.1016/j.knosys.2021.106749.
Texto completoMwitondi, Kassim S. y Raed A. Said. "Dealing with Randomness and Concept Drift in Large Datasets". Data 6, n.º 7 (19 de julio de 2021): 77. http://dx.doi.org/10.3390/data6070077.
Texto completoCabral, Danilo Rafael de Lima y Roberto Souto Maior de Barros. "Concept drift detection based on Fisher’s Exact test". Information Sciences 442-443 (mayo de 2018): 220–34. http://dx.doi.org/10.1016/j.ins.2018.02.054.
Texto completoBarros, Roberto Souto Maior y Silas Garrido T. Carvalho Santos. "A large-scale comparison of concept drift detectors". Information Sciences 451-452 (julio de 2018): 348–70. http://dx.doi.org/10.1016/j.ins.2018.04.014.
Texto completoDelany, Sarah Jane, Pádraig Cunningham, Alexey Tsymbal y Lorcan Coyle. "A case-based technique for tracking concept drift in spam filtering". Knowledge-Based Systems 18, n.º 4-5 (agosto de 2005): 187–95. http://dx.doi.org/10.1016/j.knosys.2004.10.002.
Texto completoLi, Yanhong, Deyu Li, Suge Wang y Yanhui Zhai. "Incremental entropy-based clustering on categorical data streams with concept drift". Knowledge-Based Systems 59 (marzo de 2014): 33–47. http://dx.doi.org/10.1016/j.knosys.2014.02.004.
Texto completoThaipisutikul, Tipajin. "An Adaptive Temporal-Concept Drift Model for Sequential Recommendation". ECTI Transactions on Computer and Information Technology (ECTI-CIT) 16, n.º 2 (11 de junio de 2022): 222–36. http://dx.doi.org/10.37936/ecti-cit.2022162.248019.
Texto completoTesis sobre el tema "Concept-Drift Management"
Ostovar, Alireza. "Business process drift: Detection and characterization". Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/127157/1/Alireza_Ostovar_Thesis.pdf.
Texto completoCapítulos de libros sobre el tema "Concept-Drift Management"
Patil, Malini M. "Handling Concept Drift in Data Streams by Using Drift Detection Methods". En Data Management, Analytics and Innovation, 155–66. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1274-8_12.
Texto completoWoźniak, Michał, Paweł Ksieniewicz, Bogusław Cyganek y Krzysztof Walkowiak. "Ensembles of Heterogeneous Concept Drift Detectors - Experimental Study". En Computer Information Systems and Industrial Management, 538–49. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45378-1_48.
Texto completoWang, Shenghui, Stefan Schlobach y Michel Klein. "What Is Concept Drift and How to Measure It?" En Knowledge Engineering and Management by the Masses, 241–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16438-5_17.
Texto completoMarrs, Gary R., Ray J. Hickey y Michaela M. Black. "The Impact of Latency on Online Classification Learning with Concept Drift". En Knowledge Science, Engineering and Management, 459–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15280-1_42.
Texto completoYang, Ming, William H. Hsu y Surya Teja Kallumadi. "Predictive Analytics of Social Networks". En Advances in Data Mining and Database Management, 297–333. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-5063-3.ch013.
Texto completoYang, Ming, William H. Hsu y Surya Teja Kallumadi. "Predictive Analytics of Social Networks". En Business Intelligence, 1080–116. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9562-7.ch056.
Texto completoYang, Ming, William H. Hsu y Surya Teja Kallumadi. "Predictive Analytics of Social Networks". En Social Media Marketing, 823–62. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5637-4.ch042.
Texto completoActas de conferencias sobre el tema "Concept-Drift Management"
Mak, Lee-onn y Paul Krause. "Detection & Management of Concept Drift". En 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.258538.
Texto completoGözüaçık, Ömer, Alican Büyükçakır, Hamed Bonab y Fazli Can. "Unsupervised Concept Drift Detection with a Discriminative Classifier". En CIKM '19: The 28th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3357384.3358144.
Texto completoBalog, Katalin. "The Concept and Competitiveness of Agile Organization in the Fourth Industrial Revolution’s Drift". En 25th International Scientific Conference Strategic Management and Decision Support Systems in Strategic Management. University of Novi Sad, Faculty of Economics in Subotica, 2020. http://dx.doi.org/10.46541/978-86-7233-386-2_5.
Texto completoSeeliger, Alexander, Timo Nolle y Max Mühlhäuser. "Detecting Concept Drift in Processes using Graph Metrics on Process Graphs". En S-BPM ONE '17: Conference on Subject-orientied Business Process Management. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3040565.3040566.
Texto completoTian, Hongda, Nguyen Lu Dang Khoa, Ali Anaissi, Yang Wang y Fang Chen. "Concept Drift Adaption for Online Anomaly Detection in Structural Health Monitoring". En CIKM '19: The 28th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3357384.3357816.
Texto completoSaurav, Sakti, Pankaj Malhotra, Vishnu TV, Narendhar Gugulothu, Lovekesh Vig, Puneet Agarwal y Gautam Shroff. "Online anomaly detection with concept drift adaptation using recurrent neural networks". En CoDS-COMAD '18: The ACM India Joint International Conference on Data Science & Management of Data. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3152494.3152501.
Texto completoHenke, Marcia, Eduardo Souto y Eulanda M. dos Santos. "Analysis of the evolution of features in classification problems with concept drift: Application to spam detection". En 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM). IEEE, 2015. http://dx.doi.org/10.1109/inm.2015.7140398.
Texto completoSo¨derkvist, Johan y Tomas Jansson. "IceMS: A Software for Ice Management". En ASME 2005 24th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2005. http://dx.doi.org/10.1115/omae2005-67516.
Texto completoC. Lemos Neto, Álvaro, Rodrigo A. Coelho y Cristiano L. de Castro. "An Incremental Learning approach using Long Short-Term Memory Neural Networks". En Congresso Brasileiro de Automática - 2020. sbabra, 2020. http://dx.doi.org/10.48011/asba.v2i1.1491.
Texto completoMo, Jia-hui, Peng Zou y Jin Chen. "Context of the concept drift in data mining: An empirical study on the regional economic influence to the relation between demographic attributes and credit card holder’s loyalty". En 2008 International Conference on Management Science and Engineering (ICMSE). IEEE, 2008. http://dx.doi.org/10.1109/icmse.2008.4668891.
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