Literatura académica sobre el tema "Procedure learning"
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Artículos de revistas sobre el tema "Procedure learning"
Zentall, Thomas R. "Social learning mechanisms". Interaction Studies 12, n.º 2 (21 de julio de 2011): 233–61. http://dx.doi.org/10.1075/is.12.2.03zen.
Texto completoMendelson, Shahar. "An Unrestricted Learning Procedure". Journal of the ACM 66, n.º 6 (6 de diciembre de 2019): 1–42. http://dx.doi.org/10.1145/3361699.
Texto completoTsaih, R. R. "The softening learning procedure". Mathematical and Computer Modelling 18, n.º 8 (octubre de 1993): 61–64. http://dx.doi.org/10.1016/0895-7177(93)90163-s.
Texto completoWang, Zizhuang. "Temporal-Related Convolutional-Restricted-Boltzmann-Machine Capable of Learning Relational Order via Reinforcement Learning Procedure". International Journal of Machine Learning and Computing 7, n.º 1 (febrero de 2017): 1–8. http://dx.doi.org/10.18178/ijmlc.2017.7.1.610.
Texto completoHill, Christine, Mohamad El Zein, Abhishek Agnihotri, Margo Dunlap, Angela Chang, Alison Agrawal, Sindhu Barola et al. "Endoscopic sleeve gastroplasty: the learning curve". Endoscopy International Open 05, n.º 09 (septiembre de 2017): E900—E904. http://dx.doi.org/10.1055/s-0043-115387.
Texto completoGülüstan, Filiz. "Learning curve of septoplasty procedure". Turkish Journal of Ear Nose and Throat 29, n.º 4 (31 de enero de 2020): 166–71. http://dx.doi.org/10.5606/tr-ent.2019.65265.
Texto completoAmmon, Kurt. "A learning procedure for mathematics". Annals of Mathematics and Artificial Intelligence 8, n.º 3-4 (septiembre de 1993): 407–23. http://dx.doi.org/10.1007/bf01530800.
Texto completoMlodinow, Leonard D. y Ion O. Stamatescu. "An evolutionary procedure for machine learning". International Journal of Computer & Information Sciences 14, n.º 4 (agosto de 1985): 201–19. http://dx.doi.org/10.1007/bf00997019.
Texto completoSahdan, Shafizza, Alias Masek, Noor Atikah Zainal Abidin y Juliati Jusoh. "Preliminary Study: Self-Regulated Learning Procedure". MATEC Web of Conferences 150 (2018): 05008. http://dx.doi.org/10.1051/matecconf/201815005008.
Texto completoMeehan, Anita M. "A Procedure for Learning Students' Names". Teaching of Psychology 17, n.º 2 (abril de 1990): 125–26. http://dx.doi.org/10.1207/s15328023top1702_16.
Texto completoTesis sobre el tema "Procedure learning"
Koelker, Rachel Lee Ellis Janet. "Comparing a discriminative stimulus procedure to a pairing procedure conditioning neutral social stimuli to function as conditioned reinforcers /". [Denton, Tex.] : University of North Texas, 2009. http://digital.library.unt.edu/ark:/67531/metadc12143.
Texto completoJossen, Quentin. "Unsupervised learning procedure for nonintrusive appliance load monitoring". Doctoral thesis, Universite Libre de Bruxelles, 2013. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209369.
Texto completoenergy advice. The required functionalities must however be rapidly defined if they are expected to be integrated in the future massive roll out.
Nonintrusive appliance load monitoring aims to derive appliance-specific information from the aggregate electricity consumption. While techniques have been developed since the 80’s, those mainly address the identification of previously learned appliances, from a database. Building such a database is an intrusive and tedious process which should be avoided. Whereas most recent efforts have focused on unsupervised techniques to disambiguate energy consumption into individual appliances, they usually rely on prior information about measured appliances such as the number of appliances, the number of states in each appliance as well as the power they consume in each state. This information should ideally be learned from the data. This topic will be addressed in the present research.
This work will present a framework for unsupervised learning for nonintrusive appliance
load monitoring. It aims to discover information about appliances of a household solely from its aggregate consumption data, with neither prior information nor user intervention. The learning process can be segmented into five tasks: the detection of on/off switching, the extraction of individual load signatures, the identification of
recurrent signatures, the discovery of two-state electrical devices and, finally, the elaboration
of appliance models. The first four steps will be addressed in this paper.
The suite of algorithms proposed in this work allows to discover the set of two-states electrical loads from their aggregated consumption. This, along with the evaluation
of their operating sequences, is a prerequisite to learn appliance models from the data. Results show that loads consuming power down to some dozens of watts can be learned from the data. This should encourage future researchers to consider such an unsupervised learning.
Doctorat en Sciences de l'ingénieur
info:eu-repo/semantics/nonPublished
Sjöblom, Niklas. "Evolutionary algorithms in statistical learning : Automating the optimization procedure". Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-160118.
Texto completoScania har länge jobbat med statistik men har på senare år investerat i att bli ett mer datadrivet företag och använder nu data science i nästan alla avdelningar på företaget. De algoritmer som utvecklas av data scientists måste optimeras för att kunna utnyttjas till fullo och detta är traditionellt sett en manuell och tidskrävade process. Detta examensarbete utreder om och hur väl evolutionära algoritmer kan användas för att automatisera optimeringsprocessen. Utvärderingen gjordes genom att implementera och analysera fyra varianter avgenetiska algoritmer med olika grader av komplexitet och trimningsparameterar. Algoritmen som var målet för optimering var XGBoost, som är en gradient boosted trädbaserad modell. Denna applicerades på data som tidigare hade modellerats i entävling. Resultatet visar att evolutionära algoritmer är applicerbara i att hitta bra modellermen påvisar även hur fundamentalt det är att arbeta med databearbetning innan modellering.
Koelker, Rachel Lee. "Comparing a discriminative stimulus procedure to a pairing procedure: Conditioning neutral social stimuli to function as conditioned reinforcers". Thesis, University of North Texas, 2009. https://digital.library.unt.edu/ark:/67531/metadc12143/.
Texto completoYetter, Georgette. "Acceptability of a student assistance team procedure to school staff". Diss., [Lincoln, Neb. : University of Nebraska-Lincoln], 2003. http://www.unl.edu/libr/Dissertations/2003/YetterDis.pdf.
Texto completoCampbell, Fiona. "Imitative and nonimitative social learning using a two object/two action procedure". Thesis, University College London (University of London), 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327045.
Texto completoMcManus, Jacqueline Law Faculty of Law UNSW. "Capacity-development at work: the contribution of workplace-based learning to tax administration". Awarded by:University of New South Wales. School of Law, 2007. http://handle.unsw.edu.au/1959.4/29565.
Texto completoMatoug, Mohamed Ibrahim. "Procedure-knowledge expectation (PKE) model : as an assessment tool for measuring bricklayer trainees' planning skill". Thesis, University of Salford, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.335464.
Texto completoWard, Lindsey L. "Exploring facets of classroom management to promote student learning routine, procedure, and teachers' belief systems /". Theological Research Exchange Network (TREN), 2007. http://www.tren.com/search.cfm?p088-0193.
Texto completoStaff, Donald Michael Hyten Cloyd. "Utilizing staff training methods for developing a mathetics error correction procedure in a university classroom". [Denton, Tex.] : University of North Texas, 2008. http://digital.library.unt.edu/permalink/meta-dc-9735.
Texto completoLibros sobre el tema "Procedure learning"
Stempel, Jeffrey W. Learning civil procedure. St. Paul, MN: West, 2013.
Buscar texto completoDon, Stuart, ed. Learning Canadian criminal procedure. Toronto: Carswell, 1986.
Buscar texto completo1943-, Stuart Don, ed. Learning Canadian criminal procedure. 5a ed. Scarborough, Ont: Carswell, 1998.
Buscar texto completo1943-, Stuart Don, ed. Learning Canadian criminal procedure. 6a ed. Scarborough, Ont: Carswell, 2000.
Buscar texto completoDelisle, R. J. Learning Canadian criminal procedure. 4a ed. Scarborough, Ont: Carswell, 1996.
Buscar texto completoauthor, Quigley Tim y Delisle R. J. author, eds. Learning Canadian criminal procedure. Toronto, Ontario: Carswell, a Division of Thomson Reuiters Canada Limited, 2013.
Buscar texto completo1943-, Stuart Don, ed. Learning Canadian criminal procedure. 2a ed. Scarborough, Ont., Canada: Thomson Professional Pub. Canada, 1991.
Buscar texto completoDelisle, R. J. Learning Canadian criminal procedure. Toronto: Carswell, 1986.
Buscar texto completoauthor, Quigley Tim, ed. Learning Canadian criminal procedure. Toronto, Ontario: Thomson Reuters, 2016.
Buscar texto completoDelisle, R. J. Learning Canadian criminal procedure. 3a ed. Scarborough, Ont: Carswell, 1994.
Buscar texto completoCapítulos de libros sobre el tema "Procedure learning"
Goertzel, Ben, Cassio Pennachin y Nil Geisweiller. "Integrative Procedure Learning". En Atlantis Thinking Machines, 369–89. Paris: Atlantis Press, 2014. http://dx.doi.org/10.2991/978-94-6239-030-0_23.
Texto completoFalmagne, Jean-Claude y Jean-Paul Doignon. "A Markov Chain Procedure". En Learning Spaces, 273–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-01039-2_14.
Texto completoGoertzel, Ben, Cassio Pennachin y Nil Geisweiller. "Procedure Learning as Program Learning". En Atlantis Thinking Machines, 213–16. Paris: Atlantis Press, 2014. http://dx.doi.org/10.2991/978-94-6239-030-0_12.
Texto completoHammer, Patrick y Tony Lofthouse. "Goal-Directed Procedure Learning". En Artificial General Intelligence, 77–86. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97676-1_8.
Texto completoGoertzel, Ben, Cassio Pennachin y Nil Geisweiller. "Probabilistic Evolutionary Procedure Learning". En Atlantis Thinking Machines, 239–71. Paris: Atlantis Press, 2014. http://dx.doi.org/10.2991/978-94-6239-030-0_15.
Texto completoScheffer, Thomas, Kati Hannken-Illjes y Alexander Kozin. "Procedural Presence: Failing and Learning". En Criminal Defence and Procedure, 117–37. London: Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230283114_5.
Texto completoFalmagne, Jean-Claude y Jean-Paul Doignon. "Uncovering the Latent State: A Continuous Markov Procedure". En Learning Spaces, 241–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-01039-2_13.
Texto completoAmmon, Kurt. "Some experiments with a learning procedure". En Analogical and Inductive Inference, 87–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-56004-1_6.
Texto completoGoertzel, Ben, Cassio Pennachin y Nil Geisweiller. "Procedure Learning via Adaptively Biased Hillclimbing". En Atlantis Thinking Machines, 227–38. Paris: Atlantis Press, 2014. http://dx.doi.org/10.2991/978-94-6239-030-0_14.
Texto completoSalomon, R. "Collective Learning as an Efficient Learning and Function Optimization Procedure". En Intelligent Systems Third Golden West International Conference, 77–83. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-011-7108-3_10.
Texto completoActas de conferencias sobre el tema "Procedure learning"
Chang, Chen-Huei, Chao-Chih Chang y Shu-Yuen Hwang. "Connectionist learning procedure for edge detector". En Robotics - DL tentative, editado por David P. Casasent. SPIE, 1992. http://dx.doi.org/10.1117/12.57061.
Texto completoJagamogan, Reevan Seelen, Saiful Adli Ismail, Noor Hafizah Hassan y Hafiza Abas. "Penetration Testing Procedure using Machine Learning". En 2022 4th International Conference on Smart Sensors and Application (ICSSA). IEEE, 2022. http://dx.doi.org/10.1109/icssa54161.2022.9870951.
Texto completoJacobs, Gilles y Pierre Henneaux. "Unsupervised learning procedure for NILM applications". En 2020 IEEE 20th Mediterranean Electrotechnical Conference ( MELECON). IEEE, 2020. http://dx.doi.org/10.1109/melecon48756.2020.9140477.
Texto completoLi, Lingfeng, Richard A. Paris, Conner Pinson, Yan Wang, Joseph Coco, Jamison Heard, Julie A. Adams, Daniel V. Fabbri y Bobby Bodenheimer. "Emergency Clinical Procedure Detection With Deep Learning". En 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society. IEEE, 2020. http://dx.doi.org/10.1109/embc44109.2020.9175575.
Texto completoShiina, Hiromitsu, Akiyoshi Takahashi, Ryunosuke Ito y Nobuyuki Kobayashi. "Comment Generation System for Program Procedure Learning". En 2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI). IEEE, 2018. http://dx.doi.org/10.1109/iiai-aai.2018.00018.
Texto completoElhamifar, Ehsan y Zwe Naing. "Unsupervised Procedure Learning via Joint Dynamic Summarization". En 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2019. http://dx.doi.org/10.1109/iccv.2019.00644.
Texto completoChang, Chingwei, Kwoting Fang y Chiungyu Huang. "Dynamic Knowledge Management Procedure Using Fuzzy Clustering". En 2008 Seventh International Conference on Machine Learning and Applications. IEEE, 2008. http://dx.doi.org/10.1109/icmla.2008.99.
Texto completoAbas, Marcel y Tomas Skripcak. "A modification of gradient policy in reinforcement learning procedure". En 2012 15th International Conference on Interactive Collaborative Learning (ICL). IEEE, 2012. http://dx.doi.org/10.1109/icl.2012.6402200.
Texto completoLei, Jun, Guohui Li, Jun Zhang, Dan Lu y Qiang Guo. "Online structural SVM learning by dual ascending procedure". En 2014 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC). IEEE, 2014. http://dx.doi.org/10.1109/spac.2014.6982687.
Texto completoZhohov, Roman, Alexandros Palaios, Henrik Ryden, Reza Moosavi y Joel Berglund. "Reducing Latency: Improving Handover Procedure Using Machine Learning". En 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring). IEEE, 2021. http://dx.doi.org/10.1109/vtc2021-spring51267.2021.9448875.
Texto completoInformes sobre el tema "Procedure learning"
Goetsch, Gordon J. CONSENSUS: A Statistical Learning Procedure in a Connectionist Network. Fort Belvoir, VA: Defense Technical Information Center, diciembre de 1987. http://dx.doi.org/10.21236/ada188531.
Texto completoDafny, Leemore. Entry Deterrence in Hospital Procedure Markets: A Simple Model of Learning-By-Doing. Cambridge, MA: National Bureau of Economic Research, julio de 2003. http://dx.doi.org/10.3386/w9871.
Texto completoWells, Rosalie y Joseph D. Hagman. Training Procedures for Enhancing Reserve Component Learning, Retention, and Transfer. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 1989. http://dx.doi.org/10.21236/ada217450.
Texto completoOhlsson, Stellan. The Cognitive Function of Theoretical Knowledge in Procedural Learning. Fort Belvoir, VA: Defense Technical Information Center, marzo de 1992. http://dx.doi.org/10.21236/ada248075.
Texto completoDownard, Alicia, Stephen Semmens y Bryant Robbins. Automated characterization of ridge-swale patterns along the Mississippi River. Engineer Research and Development Center (U.S.), abril de 2021. http://dx.doi.org/10.21079/11681/40439.
Texto completoOhlsson, Stellan y Ernest Reese. An Information Processing Analysis of the Function of Conceptual Understanding in the Learning of Arithmetic Procedures. Fort Belvoir, VA: Defense Technical Information Center, agosto de 1988. http://dx.doi.org/10.21236/ada202740.
Texto completoPinchuk, Olga P., Oleksandra M. Sokolyuk, Oleksandr Yu Burov y Mariya P. Shyshkina. Digital transformation of learning environment: aspect of cognitive activity of students. [б. в.], septiembre de 2019. http://dx.doi.org/10.31812/123456789/3243.
Texto completoHefetz, Abraham y Justin O. Schmidt. Use of Bee-Borne Attractants for Pollination of Nonrewarding Flowers: Model System of Male-Sterile Tomato Flowers. United States Department of Agriculture, octubre de 2003. http://dx.doi.org/10.32747/2003.7586462.bard.
Texto completoHuang, Haohang, Erol Tutumluer, Jiayi Luo, Kelin Ding, Issam Qamhia y John Hart. 3D Image Analysis Using Deep Learning for Size and Shape Characterization of Stockpile Riprap Aggregates—Phase 2. Illinois Center for Transportation, septiembre de 2022. http://dx.doi.org/10.36501/0197-9191/22-017.
Texto completoKirichek, Galina, Vladyslav Harkusha, Artur Timenko y Nataliia Kulykovska. System for detecting network anomalies using a hybrid of an uncontrolled and controlled neural network. [б. в.], febrero de 2020. http://dx.doi.org/10.31812/123456789/3743.
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