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Auswahl der wissenschaftlichen Literatur zum Thema „Dynamic machine learning“
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Zeitschriftenartikel zum Thema "Dynamic machine learning"
Tashev, Sarvar Norboboyevich. „DYNAMIC PACKET FILTERING USING MACHINE LEARNING METHODS“. American Journal of Applied Science and Technology 4, Nr. 10 (01.10.2024): 69–79. http://dx.doi.org/10.37547/ajast/volume04issue10-11.
Der volle Inhalt der QuelleLee, Peiyuan, Zhigang Huang und Yong Tang. „Trend Prediction Model of Asian Stock Market Volatility Dynamic Relationship Based on Machine Learning“. Security and Communication Networks 2022 (03.10.2022): 1–10. http://dx.doi.org/10.1155/2022/5972698.
Der volle Inhalt der QuelleChen, Hao, Tianlei Wang, Jiuwen Cao, Pierre-Paul Vidal und Yimin Yang. „Dynamic Quaternion Extreme Learning Machine“. IEEE Transactions on Circuits and Systems II: Express Briefs 68, Nr. 8 (August 2021): 3012–16. http://dx.doi.org/10.1109/tcsii.2021.3067014.
Der volle Inhalt der QuelleZheng, Li-E., Shrishti Barethiya, Erik Nordquist und Jianhan Chen. „Machine Learning Generation of Dynamic Protein Conformational Ensembles“. Molecules 28, Nr. 10 (12.05.2023): 4047. http://dx.doi.org/10.3390/molecules28104047.
Der volle Inhalt der QuelleKumar, K. Bindu, K. R. Remesh Babu, Ramesh Unnikrishnan und U. Sangeetha. „Dynamic Behaviour Modelling of Magneto-Rheological Fluid Damper Using Machine Learning“. Indian Journal Of Science And Technology 16, Nr. 45 (13.12.2023): 4233–43. http://dx.doi.org/10.17485/ijst/v16i45.1669.
Der volle Inhalt der QuelleLennie, Matthew, Johannes Steenbuck, Bernd R. Noack und Christian Oliver Paschereit. „Cartographing dynamic stall with machine learning“. Wind Energy Science 5, Nr. 2 (29.06.2020): 819–38. http://dx.doi.org/10.5194/wes-5-819-2020.
Der volle Inhalt der QuelleStarzyk, J. A., und F. Wang. „Dynamic Probability Estimator for Machine Learning“. IEEE Transactions on Neural Networks 15, Nr. 2 (März 2004): 298–308. http://dx.doi.org/10.1109/tnn.2004.824254.
Der volle Inhalt der QuelleDubach, Christophe, Timothy M. Jones und Edwin V. Bonilla. „Dynamic microarchitectural adaptation using machine learning“. ACM Transactions on Architecture and Code Optimization 10, Nr. 4 (Dezember 2013): 1–28. http://dx.doi.org/10.1145/2541228.2541238.
Der volle Inhalt der QuelleYadav, Ram Ashish. „Dynamic Playlist Generation using Machine Learning“. INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, Nr. 05 (10.05.2024): 1–5. http://dx.doi.org/10.55041/ijsrem32579.
Der volle Inhalt der QuelleWANG Peng und MAIMAITINIYAZI Maimaitiabudula. „Quantum Dynamics of Machine Learning“. Acta Physica Sinica 74, Nr. 6 (2025): 0. https://doi.org/10.7498/aps.74.20240999.
Der volle Inhalt der QuelleDissertationen zum Thema "Dynamic machine learning"
Höstklint, Niklas, und Jesper Larsson. „Dynamic Test Case Selection using Machine Learning“. Thesis, KTH, Hälsoinformatik och logistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-296634.
Der volle Inhalt der QuelleTestning av kod är en avgörande del för alla mjukvaruproducerande företag, för att säkerställa att ingen felaktig kod som kan ha skadlig påverkan publiceras. Hos Ericsson är testning av kod innan det ska publiceras en väldigt dyr process som kan ta flera timmar. Vid tiden denna rapport skrivs så körs varenda test för all inlämnad kod. Denna rapport har som mål att lösa/reducera problemet genom att bygga en modell med maskininlärning som avgör vilka tester som ska köras, så onödiga tester lämnas utanför vilket i sin tur sparar tid och resurser. Dock är det viktigt att hitta alla misslyckade tester, eftersom att tillåta dessa passera till produktionen kan innebära alla möjliga olika ekonomiska, miljömässiga och sociala konsekvenser. Resultaten visar att det finns stor potential i flera olika typer av modeller. En linjär regressionsmodell hittade 92% av alla fel inom att 25% av alla test kategorier körts. Den linjära modellen träffar dock en platå innan den hittar de sista felen. Om det är essentiellt att hitta 100% av felen, så visade sig en support vector regressionsmodell vara mest effektiv, då den var den enda modellen som lyckades hitta 100% av alla fel inom att 90% alla test kategorier hade körts.
Rowe, Michael C. (Michael Charles). „A Machine Learning Method Suitable for Dynamic Domains“. Thesis, University of North Texas, 1996. https://digital.library.unt.edu/ark:/67531/metadc278720/.
Der volle Inhalt der QuelleNarmack, Kirilll. „Dynamic Speed Adaptation for Curves using Machine Learning“. Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233545.
Der volle Inhalt der QuelleMorgondagens fordon kommer att vara mer sofistikerade, intelligenta och säkra än dagens fordon. Framtiden lutar mot fullständigt autonoma fordon. Detta examensarbete tillhandahåller en datadriven lösning för ett hastighetsanpassningssystem som kan beräkna ett fordons hastighet i kurvor som är lämpligt för förarens körstil, vägens egenskaper och rådande väder. Ett hastighetsanpassningssystem för kurvor har som mål att beräkna en fordonshastighet för kurvor som kan användas i Advanced Driver Assistance Systems (ADAS) eller Autonomous Driving (AD) applikationer. Detta examensarbete utfördes på Volvo Car Corporation. Litteratur kring hastighetsanpassningssystem samt faktorer som påverkar ett fordons hastighet i kurvor studerades. Naturalistisk bilkörningsdata samlades genom att köra bil samt extraherades från Volvos databas och bearbetades. Ett nytt hastighetsanpassningssystem uppfanns, implementerades samt utvärderades. Hastighetsanpassningssystemet visade sig vara kapabelt till att beräkna en lämplig fordonshastighet för förarens körstil under rådande väderförhållanden och vägens egenskaper. Två olika artificiella neuronnätverk samt två matematiska modeller användes för att beräkna fordonets hastighet. Dessa metoder jämfördes och utvärderades.
Sîrbu, Adela-Maria. „Dynamic machine learning for supervised and unsupervised classification“. Thesis, Rouen, INSA, 2016. http://www.theses.fr/2016ISAM0002/document.
Der volle Inhalt der QuelleThe research direction we are focusing on in the thesis is applying dynamic machine learning models to salve supervised and unsupervised classification problems. We are living in a dynamic environment, where data is continuously changing and the need to obtain a fast and accurate solution to our problems has become a real necessity. The particular problems that we have decided te approach in the thesis are pedestrian recognition (a supervised classification problem) and clustering of gene expression data (an unsupervised classification. problem). The approached problems are representative for the two main types of classification and are very challenging, having a great importance in real life.The first research direction that we approach in the field of dynamic unsupervised classification is the problem of dynamic clustering of gene expression data. Gene expression represents the process by which the information from a gene is converted into functional gene products: proteins or RNA having different roles in the life of a cell. Modern microarray technology is nowadays used to experimentally detect the levels of expressions of thousand of genes, across different conditions and over time. Once the gene expression data has been gathered, the next step is to analyze it and extract useful biological information. One of the most popular algorithms dealing with the analysis of gene expression data is clustering, which involves partitioning a certain data set in groups, where the components of each group are similar to each other. In the case of gene expression data sets, each gene is represented by its expression values (features), at distinct points in time, under the monitored conditions. The process of gene clustering is at the foundation of genomic studies that aim to analyze the functions of genes because it is assumed that genes that are similar in their expression levels are also relatively similar in terms of biological function.The problem that we address within the dynamic unsupervised classification research direction is the dynamic clustering of gene expression data. In our case, the term dynamic indicates that the data set is not static, but it is subject to change. Still, as opposed to the incremental approaches from the literature, where the data set is enriched with new genes (instances) during the clustering process, our approaches tackle the cases when new features (expression levels for new points in time) are added to the genes already existing in the data set. To our best knowledge, there are no approaches in the literature that deal with the problem of dynamic clustering of gene expression data, defined as above. In this context we introduced three dynamic clustering algorithms which are able to handle new collected gene expression levels, by starting from a previous obtained partition, without the need to re-run the algorithm from scratch. Experimental evaluation shows that our method is faster and more accurate than applying the clustering algorithm from scratch on the feature extended data set
Boulegane, Dihia. „Machine learning algorithms for dynamic Internet of Things“. Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT048.
Der volle Inhalt der QuelleWith the rapid growth of Internet-of-Things (IoT) devices and sensors, sources that are continuously releasing and curating vast amount of data at high pace in the form of stream. The ubiquitous data streams are essential for data driven decisionmaking in different business sectors using Artificial Intelligence (AI) and Machine Learning (ML) techniques in order to extract valuable knowledge and turn it to appropriate actions. Besides, the data being collected is often associated with a temporal indicator, referred to as temporal data stream that is a potentially infinite sequence of observations captured over time at regular intervals, but not necessarily. Forecasting is a challenging tasks in the field of AI and aims at understanding the process generating the observations over time based on past data in order to accurately predict future behavior. Stream Learning is the emerging research field which focuses on learning from infinite and evolving data streams. The thesis tackles dynamic model combination that achieves competitive results despite their high computational costs in terms of memory and time. We study several approaches to estimate the predictive performance of individual forecasting models according to the data and contribute by introducing novel windowing and meta-learning based methods to cope with evolving data streams. Subsequently, we propose different selection methods that aim at constituting a committee of accurate and diverse models. The predictions of these models are then weighted and aggregated. The second part addresses model compression that aims at building a single model to mimic the behavior of a highly performing and complex ensemble while reducing its complexity. Finally, we present the first streaming competition ”Real-time Machine Learning Competition on Data Streams”, at the IEEE Big Data 2019 conference, using the new SCALAR platform
Brun, Yuriy 1981. „Software fault identification via dynamic analysis and machine learning“. Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/17939.
Der volle Inhalt der QuelleIncludes bibliographical references (p. 65-67).
I propose a technique that identifies program properties that may indicate errors. The technique generates machine learning models of run-time program properties known to expose faults, and applies these models to program properties of user-written code to classify and rank properties that may lead the user to errors. I evaluate an implementation of the technique, the Fault Invariant Classifier, that demonstrates the efficacy of the error finding technique. The implementation uses dynamic invariant detection to generate program properties. It uses support vector machine and decision tree learning tools to classify those properties. Given a set of properties produced by the program analysis, some of which are indicative of errors, the technique selects a subset of properties that are most likely to reveal an error. The experimental evaluation over 941,000 lines of code, showed that a user must examine only the 2.2 highest-ranked properties for C programs and 1.7 for Java programs to find a fault-revealing property. The technique increases the relevance (the concentration of properties that reveal errors) by a factor of 50 on average for C programs, and 4.8 for Java programs.
by Yuriy Brun.
M.Eng.
Emani, Murali Krishna. „Adaptive parallelism mapping in dynamic environments using machine learning“. Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/10469.
Der volle Inhalt der QuelleDahlberg, Love. „Dynamic algorithm selection for machine learning on time series“. Thesis, Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-72576.
Der volle Inhalt der QuelleCaceres, Carlos Antonio. „Machine Learning Techniques for Gesture Recognition“. Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/52556.
Der volle Inhalt der QuelleMaster of Science
Botlani-Esfahani, Mohsen. „Modeling of Dynamic Allostery in Proteins Enabled by Machine Learning“. Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6804.
Der volle Inhalt der QuelleBücher zum Thema "Dynamic machine learning"
Gultekin, San. Dynamic Machine Learning with Least Square Objectives. [New York, N.Y.?]: [publisher not identified], 2019.
Den vollen Inhalt der Quelle findenBennaceur, Amel, Reiner Hähnle und Karl Meinke, Hrsg. Machine Learning for Dynamic Software Analysis: Potentials and Limits. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96562-8.
Der volle Inhalt der QuelleIEEE, International Symposium on Approximate Dynamic Programming and Reinforcement Learning (1st 2007 Honolulu Hawaii). 2007 IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning: Honolulu, HI, 1-5 April 2007. Piscataway, NJ: IEEE, 2007.
Den vollen Inhalt der Quelle findenHinders, Mark K. Intelligent Feature Selection for Machine Learning Using the Dynamic Wavelet Fingerprint. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49395-0.
Der volle Inhalt der QuelleIEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning (1st 2007 Honolulu, Hawaii). 2007 IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning: Honolulu, HI, 1-5 April 2007. Piscataway, NJ: IEEE, 2007.
Den vollen Inhalt der Quelle findenIEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning (1st 2007 Honolulu, Hawaii). 2007 IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning: Honolulu, HI, 1-5 April 2007. Piscataway, NJ: IEEE, 2007.
Den vollen Inhalt der Quelle findenAchmad, Widodo, Hrsg. Introduction of intelligent machine fault diagnosis and prognosis. New York: Nova Science Publishers, 2009.
Den vollen Inhalt der Quelle findenRussell, David W. The BOXES Methodology: Black Box Dynamic Control. London: Springer London, 2012.
Den vollen Inhalt der Quelle findenHayes-Roth, Barbara. An architecture for adaptive intelligent systems. Stanford, Calif: Stanford University, Dept. of Computer Science, 1993.
Den vollen Inhalt der Quelle findenDuriez, Thomas, Steven L. Brunton und Bernd R. Noack. Machine Learning Control – Taming Nonlinear Dynamics and Turbulence. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-40624-4.
Der volle Inhalt der QuelleBuchteile zum Thema "Dynamic machine learning"
Webb, Geoffrey I., Johannes Fürnkranz, Johannes Fürnkranz, Johannes Fürnkranz, Geoffrey Hinton, Claude Sammut, Joerg Sander et al. „Dynamic Programming“. In Encyclopedia of Machine Learning, 298–308. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_237.
Der volle Inhalt der QuelleWebb, Geoffrey I., Johannes Fürnkranz, Johannes Fürnkranz, Johannes Fürnkranz, Geoffrey Hinton, Claude Sammut, Joerg Sander et al. „Dynamic Systems“. In Encyclopedia of Machine Learning, 308. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_239.
Der volle Inhalt der QuelleWebb, Geoffrey I., Johannes Fürnkranz, Johannes Fürnkranz, Johannes Fürnkranz, Geoffrey Hinton, Claude Sammut, Joerg Sander et al. „Dynamic Bayesian Network“. In Encyclopedia of Machine Learning, 298. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_234.
Der volle Inhalt der QuelleWebb, Geoffrey I., Johannes Fürnkranz, Johannes Fürnkranz, Johannes Fürnkranz, Geoffrey Hinton, Claude Sammut, Joerg Sander et al. „Dynamic Decision Networks“. In Encyclopedia of Machine Learning, 298. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_235.
Der volle Inhalt der QuelleWebb, Geoffrey I., Johannes Fürnkranz, Johannes Fürnkranz, Johannes Fürnkranz, Geoffrey Hinton, Claude Sammut, Joerg Sander et al. „Dynamic Memory Model“. In Encyclopedia of Machine Learning, 298. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_236.
Der volle Inhalt der QuelleKakas, Antonis C., David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb et al. „Approximate Dynamic Programming“. In Encyclopedia of Machine Learning, 39. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_26.
Der volle Inhalt der QuelleWebb, Geoffrey I., Eamonn Keogh, Risto Miikkulainen, Risto Miikkulainen und Michele Sebag. „Neuro-Dynamic Programming“. In Encyclopedia of Machine Learning, 716. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_588.
Der volle Inhalt der QuelleBuhmann, M. D., Prem Melville, Vikas Sindhwani, Novi Quadrianto, Wray L. Buntine, Luís Torgo, Xinhua Zhang et al. „Relational Dynamic Programming“. In Encyclopedia of Machine Learning, 851. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_718.
Der volle Inhalt der QuelleMartin, Eric, Samuel Kaski, Fei Zheng, Geoffrey I. Webb, Xiaojin Zhu, Ion Muslea, Kai Ming Ting et al. „Symbolic Dynamic Programming“. In Encyclopedia of Machine Learning, 946–54. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_806.
Der volle Inhalt der QuelleBuhmann, M. D., Prem Melville, Vikas Sindhwani, Novi Quadrianto, Wray L. Buntine, Luís Torgo, Xinhua Zhang et al. „Real-Time Dynamic Programming“. In Encyclopedia of Machine Learning, 829. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_701.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Dynamic machine learning"
Lin, Yu Chi, und Po-Wen Chi. „Adaptive Machine Learning Model for Dynamic Field Selection“. In 2024 19th Asia Joint Conference on Information Security (AsiaJCIS), 151–56. IEEE, 2024. http://dx.doi.org/10.1109/asiajcis64263.2024.00032.
Der volle Inhalt der QuelleGuo, Ben, Ming-yan Wang und Jian Zhang. „A Dynamic Fuzzy Neural Networks Controller for Dynamic Load Simulator“. In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.259042.
Der volle Inhalt der QuelleYing Gui, Xue-Qin Zhu und Wen-Lin Song. „The Tracking Dynamical Particle Swarm Optimizer for dynamic environments“. In 2008 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2008. http://dx.doi.org/10.1109/icmlc.2008.4621020.
Der volle Inhalt der QuellePeleshchak, Ivan, Diana Koshtura, Mykhailo Luchkevych und Volodymyr Tymchuk. „Classification of Dynamic Objects Using a Multilayer Perceptron“. In Machine Learning Workshop at CoLInS 2024. CoLInS, 2024. http://dx.doi.org/10.31110/colins/2024-1/014.
Der volle Inhalt der QuelleGaitang Wang und Ping Li. „Dynamic Adaboost ensemble extreme learning machine“. In 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icacte.2010.5579726.
Der volle Inhalt der QuelleDhiman, Gaurav, und Tajana Rosing. „Dynamic Power Management Using Machine Learning“. In 2006 IEEE/ACM International Conference on Computer Aided Design. IEEE, 2006. http://dx.doi.org/10.1109/iccad.2006.320115.
Der volle Inhalt der QuelleDhiman, Gaurav, und Tajana Simunic Rosing. „Dynamic power management using machine learning“. In the 2006 IEEE/ACM international conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1233501.1233656.
Der volle Inhalt der QuelleChebrolu, Chandan Sai, Chung-Horng Lung und Samuel A. Ajila. „Dynamic Packet Filtering Using Machine Learning“. In 2022 IEEE 23rd International Conference on Information Reuse and Integration for Data Science (IRI). IEEE, 2022. http://dx.doi.org/10.1109/iri54793.2022.00053.
Der volle Inhalt der QuelleZhang, Yingying, und Yue Shi. „Constructing Dynamic Honeypot Using Machine Learning“. In ICCSIE 2023: 8th International Conference on Cyber Security and Information Engineering. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3617184.3618056.
Der volle Inhalt der QuelleMin, Fan, Qi-He Liu, Hong-Bin Cai und Zhong-Jian Bai. „Dynamic Discretization: A Combination Approach“. In 2007 International Conference on Machine Learning and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370785.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Dynamic machine learning"
Sgroi, Michael Jacobson. Dynamic and System Agnostic Malware Detection Via Machine Learning. Ames (Iowa): Iowa State University, Januar 2018. http://dx.doi.org/10.31274/cc-20240624-572.
Der volle Inhalt der QuelleGonzalez Pibernat, Gabriel, und Miguel Mascaró Portells. Dynamic structure of single-layer neural networks. Fundación Avanza, Mai 2023. http://dx.doi.org/10.60096/fundacionavanza/2392022.
Der volle Inhalt der QuelleAo, Tommy, Brendan Donohoe, Carianne Martinez, Marcus Knudson, Dane Morgan, Mark Rodriguez und James Lane. LDRD 226360 Final Project Report: Simulated X-ray Diffraction and Machine Learning for Optimizing Dynamic Experiment Analysis. Office of Scientific and Technical Information (OSTI), Oktober 2022. http://dx.doi.org/10.2172/1891594.
Der volle Inhalt der QuelleBailey Bond, Robert, Pu Ren, James Fong, Hao Sun und Jerome F. Hajjar. Physics-informed Machine Learning Framework for Seismic Fragility Analysis of Steel Structures. Northeastern University, August 2024. http://dx.doi.org/10.17760/d20680141.
Der volle Inhalt der QuelleHovakimyan, Naira, Hunmin Kim, Wenbin Wan und Chuyuan Tao. Safe Operation of Connected Vehicles in Complex and Unforeseen Environments. Illinois Center for Transportation, August 2022. http://dx.doi.org/10.36501/0197-9191/22-016.
Der volle Inhalt der QuellePerdigão, Rui A. P., und Julia Hall. Augmented Post-Quantum Synergistic Manifold Intelligence for Complex System Dynamics and Coevolutionary Multi-Hazards. Synergistic Manifolds, Dezember 2024. https://doi.org/10.46337/241211.
Der volle Inhalt der QuelleMuñoz-Martínez, Jonathan Alexander, David Orozco und Mario A. Ramos-Veloza. Tweeting Inflation: Real-Time measures of Inflation Perception in Colombia. Banco de la República, November 2023. http://dx.doi.org/10.32468/be.1256.
Der volle Inhalt der QuelleLewis, James, Aldo Romero, Oleg Prozhdo und Marcus Hanwell. Machine-Learning for Excited-State Dynamics. Office of Scientific and Technical Information (OSTI), März 2022. http://dx.doi.org/10.2172/1848053.
Der volle Inhalt der QuelleКів, Арнольд Юхимович, Володимир Миколайович Соловйов, Сергій Олексійович Семеріков, Hanna B. Danylchuk, Liubov O. Kibalnyk, Andriy V. Matviychuk, Andrii M. Striuk et al. Machine learning for prediction of emergent economy dynamics. Криворізький державний педагогічний університет, Dezember 2021. http://dx.doi.org/10.31812/123456789/6973.
Der volle Inhalt der QuelleJääskeläinen, Emmihenna. Construction of reliable albedo time series. Finnish Meteorological Institute, September 2023. http://dx.doi.org/10.35614/isbn.9789523361782.
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