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Статті в журналах з теми "Application of learning theory"
Lowndes, Diane EN(G), Anwen EN(G) Boult, and Lap Yee (M) Wong EN. "An application of learning theory." Nursing Standard 5, no. 51 (September 11, 1991): 28–30. http://dx.doi.org/10.7748/ns.5.51.28.s47.
Повний текст джерелаNajim, K., and G. Oppenheim. "Learning systems: Theory and application." IEE Proceedings E Computers and Digital Techniques 138, no. 4 (1991): 183. http://dx.doi.org/10.1049/ip-e.1991.0025.
Повний текст джерелаFarhan, Bayan Yousef. "Application Of Path-Goal Leadership Theory And Learning Theory In A Learning Organization." Journal of Applied Business Research (JABR) 34, no. 1 (December 29, 2017): 13–22. http://dx.doi.org/10.19030/jabr.v34i1.10088.
Повний текст джерелаLandry-Meyer, Laura, Su Yun Bae, John Zibbel, Susan Peet, and Deborah G. Wooldridge. "Transformative Learning." International Journal of Adult Vocational Education and Technology 10, no. 4 (October 2019): 1–15. http://dx.doi.org/10.4018/ijavet.2019100101.
Повний текст джерелаJin, Yuxi. "Development and Application of Social Learning Theory." Learning & Education 10, no. 7 (June 7, 2022): 183. http://dx.doi.org/10.18282/l-e.v10i7.3002.
Повний текст джерелаJoseph, Sylvia. "Social Learning Theory Application on Bullying Phenomenon." Journal of International Business Research and Marketing 6, no. 6 (September 2021): 7–12. http://dx.doi.org/10.18775/10.18775/jibrm.1849-8558.2015.66.3001.
Повний текст джерелаChauhan, S. P., E. F. Magann, C. B. McAninch, R. B. Gherman, and J. C. Morrison. "Application of learning theory to obstetric maloccurrence." Journal of Maternal-Fetal & Neonatal Medicine 13, no. 3 (January 2003): 203–7. http://dx.doi.org/10.1080/jmf.13.3.203.207.
Повний текст джерелаPrice, Vincent, and John Archbold. "Development and application of social learning theory." British Journal of Nursing 4, no. 21 (November 23, 1995): 1263–68. http://dx.doi.org/10.12968/bjon.1995.4.21.1263.
Повний текст джерелаNorman, Leanne. "Learning in sports coaching: theory and application." Leisure/Loisir 40, no. 4 (October 2016): 495–96. http://dx.doi.org/10.1080/14927713.2016.1276730.
Повний текст джерелаChauhan, S. P., E. F. Magann, C. B. McAninch, R. B. Gherman, and J. C. Horrison. "Application of Learning Theory to Obstetric Maloccurrence." Obstetrical & Gynecological Survey 58, no. 10 (October 2003): 650–52. http://dx.doi.org/10.1097/01.ogx.0000088882.02275.b2.
Повний текст джерелаДисертації з теми "Application of learning theory"
Cleeton, G. "Development and application of a theory of learning barriers." Thesis, Keele University, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.306150.
Повний текст джерелаHu, Qiao Ph D. Massachusetts Institute of Technology. "Application of statistical learning theory to plankton image analysis." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/39206.
Повний текст джерелаIncludes bibliographical references (leaves 155-173).
A fundamental problem in limnology and oceanography is the inability to quickly identify and map distributions of plankton. This thesis addresses the problem by applying statistical machine learning to video images collected by an optical sampler, the Video Plankton Recorder (VPR). The research is focused on development of a real-time automatic plankton recognition system to estimate plankton abundance. The system includes four major components: pattern representation/feature measurement, feature extraction/selection, classification, and abundance estimation. After an extensive study on a traditional learning vector quantization (LVQ) neural network (NN) classifier built on shape-based features and different pattern representation methods, I developed a classification system combined multi-scale cooccurrence matrices feature with support vector machine classifier. This new method outperforms the traditional shape-based-NN classifier method by 12% in classification accuracy. Subsequent plankton abundance estimates are improved in the regions of low relative abundance by more than 50%. Both the NN and SVM classifiers have no rejection metrics. In this thesis, two rejection metrics were developed.
(cont.) One was based on the Euclidean distance in the feature space for NN classifier. The other used dual classifier (NN and SVM) voting as output. Using the dual-classification method alone yields almost as good abundance estimation as human labeling on a test-bed of real world data. However, the distance rejection metric for NN classifier might be more useful when the training samples are not "good" ie, representative of the field data. In summary, this thesis advances the current state-of-the-art plankton recognition system by demonstrating multi-scale texture-based features are more suitable for classifying field-collected images. The system was verified on a very large real-world dataset in systematic way for the first time. The accomplishments include developing a multi-scale occurrence matrices and support vector machine system, a dual-classification system, automatic correction in abundance estimation, and ability to get accurate abundance estimation from real-time automatic classification. The methods developed are generic and are likely to work on range of other image classification applications.
by Qiao Hu.
Ph.D.
Plaza, Cecilia Maria. "The Application of Transformative Learning Theory to Curricular Evaluation." Diss., The University of Arizona, 2006. http://hdl.handle.net/10150/194354.
Повний текст джерелаShi, Bin. "A Mathematical Framework on Machine Learning: Theory and Application." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3876.
Повний текст джерелаMouton, Hildegarde Suzanne. "Reinforcement learning : theory, methods and application to decision support systems." Thesis, Stellenbosch : University of Stellenbosch, 2010. http://hdl.handle.net/10019.1/5304.
Повний текст джерелаENGLISH ABSTRACT: In this dissertation we study the machine learning subfield of Reinforcement Learning (RL). After developing a coherent background, we apply a Monte Carlo (MC) control algorithm with exploring starts (MCES), as well as an off-policy Temporal-Difference (TD) learning control algorithm, Q-learning, to a simplified version of the Weapon Assignment (WA) problem. For the MCES control algorithm, a discount parameter of τ = 1 is used. This gives very promising results when applied to 7 × 7 grids, as well as 71 × 71 grids. The same discount parameter cannot be applied to the Q-learning algorithm, as it causes the Q-values to diverge. We take a greedy approach, setting ε = 0, and vary the learning rate (α ) and the discount parameter (τ). Experimentation shows that the best results are found with set to 0.1 and constrained in the region 0.4 ≤ τ ≤ 0.7. The MC control algorithm with exploring starts gives promising results when applied to the WA problem. It performs significantly better than the off-policy TD algorithm, Q-learning, even though it is almost twice as slow. The modern battlefield is a fast paced, information rich environment, where discovery of intent, situation awareness and the rapid evolution of concepts of operation and doctrine are critical success factors. Combining the techniques investigated and tested in this work with other techniques in Artificial Intelligence (AI) and modern computational techniques may hold the key to solving some of the problems we now face in warfare.
AFRIKAANSE OPSOMMING: Die fokus van hierdie verhandeling is die masjienleer-algoritmes in die veld van versterkingsleer. ’n Koherente agtergrond van die veld word gevolg deur die toepassing van ’n Monte Carlo (MC) beheer-algoritme met ondersoekende begintoestande, sowel as ’n afbeleid Temporale-Verskil beheer-algoritme, Q-leer, op ’n vereenvoudigde weergawe van die wapentoekenningsprobleem. Vir die MC beheer-algoritme word ’n afslagparameter van τ = 1 gebruik. Dit lewer belowende resultate wanneer toegepas op 7 × 7 roosters, asook op 71 × 71 roosters. Dieselfde afslagparameter kan nie op die Q-leer algoritme toegepas word nie, aangesien dit veroorsaak dat die Q-waardes divergeer. Ons neem ’n gulsige aanslag deur die gulsigheidsparameter te verstel na ε = 0. Ons varieer dan die leertempo ( α) en die afslagparameter (τ). Die beste eksperimentele resultate is behaal wanneer = 0.1 en as die afslagparameter vasgehou word in die gebied 0.4 ≤ τ ≤ 0.7. Die MC beheer-algoritme lewer belowende resultate wanneer toegepas op die wapentoekenningsprobleem. Dit lewer beduidend beter resultate as die Q-leer algoritme, al neem dit omtrent twee keer so lank om uit te voer. Die moderne slagveld is ’n omgewing ryk aan inligting, waar dit kritiek belangrik is om vinnig die vyand se planne te verstaan, om bedag te wees op die omgewing en die konteks van gebeure, en waar die snelle ontwikkeling van die konsepte van operasie en doktrine lei tot sukses. Die tegniekes wat in die verhandeling ondersoek en getoets is, en ander kunsmatige intelligensie tegnieke en moderne berekeningstegnieke saamgesnoer, mag dalk die sleutel hou tot die oplossing van die probleme wat ons tans in die gesig staar in oorlogvoering.
Gianvecchio, Steven. "Application of information theory and statistical learning to anomaly detection." W&M ScholarWorks, 2010. https://scholarworks.wm.edu/etd/1539623563.
Повний текст джерелаCollins, Andrew. "Evaluating reinforcement learning for game theory application learning to price airline seats under competition." Thesis, University of Southampton, 2009. https://eprints.soton.ac.uk/69751/.
Повний текст джерелаNarasimha, Rajesh. "Application of Information Theory and Learning to Network and Biological Tomography." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19889.
Повний текст джерелаJalalzai, Hamid. "Learning from multivariate extremes : theory and application to natural language processing." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAT043.
Повний текст джерелаExtremes surround us and appear in a large variety of data. Natural data likethe ones related to environmental sciences contain extreme measurements; inhydrology, for instance, extremes may correspond to floods and heavy rainfalls or on the contrary droughts. Data related to human activity can also lead to extreme situations; in the case of bank transactions, the money allocated to a sale may be considerable and exceed common transactions. The analysis of this phenomenon is one of the basis of fraud detection. Another example related to humans is the frequency of encountered words. Some words are ubiquitous while others are rare. No matter the context, extremes which are rare by definition, correspond to uncanny data. These events are of particular concern because of the disastrous impact they may have. Extreme data, however, are less considered in modern statistics and applied machine learning, mainly because they are substantially scarce: these events are out numbered –in an era of so-called ”big data”– by the large amount of classical and non-extreme data that corresponds to the bulk of a distribution. Thus, the wide majority of machine learning tools and literature may not be well-suited or even performant on the distributional tails where extreme observations occur. Through this dissertation, the particular challenges of working with extremes are detailed and methods dedicated to them are proposed. The first part of the thesisis devoted to statistical learning in extreme regions. In Chapter 4, non-asymptotic bounds for the empirical angular measure are studied. Here, a pre-established anomaly detection scheme via minimum volume set on the sphere, is further im-proved. Chapter 5 addresses empirical risk minimization for binary classification of extreme samples. The resulting non-parametric analysis and guarantees are detailed. The approach is particularly well suited to treat new samples falling out of the convex envelop of encountered data. This extrapolation property is key to designing new embeddings achieving label preserving data augmentation. Chapter 6 focuses on the challenge of learning the latter heavy-tailed (and to be precise regularly varying) representation from a given input distribution. Empirical results show that the designed representation allows better classification performanceon extremes and leads to the generation of coherent sentences. Lastly, Chapter7 analyses the dependence structure of multivariate extremes. By noticing that extremes tend to concentrate on particular clusters where features tend to be recurrently large simulatenously, we define an optimization problem that identifies the aformentioned subgroups through weighted means of features
Scaggs, Anne Marie. "Student Perspectives on Application of Theory to Practice in Field Practicums." ScholarWorks, 2018. https://scholarworks.waldenu.edu/dissertations/6112.
Повний текст джерелаКниги з теми "Application of learning theory"
Long, Huey B. Self-directed learning: Application & theory. [Athens, Ga.]: Adult Education Department, [University of Georgia, 1988.
Знайти повний текст джерелаHassanien, Aboul Ella, ed. Machine Learning Paradigms: Theory and Application. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-02357-7.
Повний текст джерелаManifold learning theory and applications. Boca Raton, Fla: CRC, 2012.
Знайти повний текст джерелаClinical applications of learning theory. Hove: Psychology Press, 2011.
Знайти повний текст джерелаNajim, K. Learning automata: Theory and applications. Oxford, OX, U.K: Pergamon, 1994.
Знайти повний текст джерелаAlfred North Whitehead on learning and education: Theory and application. Newcastle: Cambridge Scholars Press, 2005.
Знайти повний текст джерелаPonton, Michael K. A quasi-linear behavioral model and an application to self-directed learning. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1999.
Знайти повний текст джерелаLearning curves: Theory, models, and applications. Boca Raton: CRC Press, 2011.
Знайти повний текст джерелаHuang, Kaizhu, Amir Hussain, Qiu-Feng Wang, and Rui Zhang, eds. Deep Learning: Fundamentals, Theory and Applications. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-06073-2.
Повний текст джерелаHanson, Stephen José, Werner Remmele, and Ronald L. Rivest, eds. Machine Learning: From Theory to Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-56483-7.
Повний текст джерелаЧастини книг з теми "Application of learning theory"
Tanaka, Akinori, Akio Tomiya, and Koji Hashimoto. "Application to Superstring Theory." In Deep Learning and Physics, 173–93. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6108-9_12.
Повний текст джерелаBanyard, Philip, and Nicky Hayes. "Learning and remembering." In Psychology: Theory and Application, 257–322. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4899-3007-1_5.
Повний текст джерелаDobberfuhl-Quinlan, Jennifer. "Exploring Sociocultural Theory Application in Online Language Courses." In Learning and Collaboration Technologies. Learning and Teaching, 265–75. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91152-6_21.
Повний текст джерелаShawky, Doaa, and Ashraf Badawi. "Towards a Personalized Learning Experience Using Reinforcement Learning." In Machine Learning Paradigms: Theory and Application, 169–87. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02357-7_8.
Повний текст джерелаLandauer, Tom. "Latent Semantic Analysis: Theory, Method and Application." In Computer Support for Collaborative Learning, 742–43. New York: Routledge, 2023. http://dx.doi.org/10.4324/9781315045467-202.
Повний текст джерелаCaramanis, Constantine, and Shie Mannor. "An Inequality for Nearly Log-Concave Distributions with Applications to Learning." In Learning Theory, 534–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27819-1_37.
Повний текст джерелаLowe, Richard. "Animation of Diagrams: An Aid to Learning?" In Theory and Application of Diagrams, 475–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44590-0_40.
Повний текст джерелаCâmara Pereira, Francisco, and Amíilcar Cardoso. "Clouds: A Module for Automatic Learning of Concept Maps." In Theory and Application of Diagrams, 468–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44590-0_38.
Повний текст джерелаRamadan, Rabie A., and Ahmed B. Altamimi. "BCLO—Brainstorming and Collaborative Learning Optimization Algorithms." In Machine Learning Paradigms: Theory and Application, 393–412. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02357-7_19.
Повний текст джерелаSaidi, Rania, Waad Bouaguel, and Nadia Essoussi. "Hybrid Feature Selection Method Based on the Genetic Algorithm and Pearson Correlation Coefficient." In Machine Learning Paradigms: Theory and Application, 3–24. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02357-7_1.
Повний текст джерелаТези доповідей конференцій з теми "Application of learning theory"
Koohang, Alex, Liz Riley, Terry Smith, and Jeanne Schreurs. "E-Learning and Constructivism: From Theory to Application." In InSITE 2009: Informing Science + IT Education Conference. Informing Science Institute, 2009. http://dx.doi.org/10.28945/3321.
Повний текст джерелаSalamah, Umi, Nuril Mufidah, Idrus Muchsin Bin Agil, and Iffah Maulana Putri Hanum Soumena. "Application of Behavioristic Learning Theory in Learning “Ta’lim Afkar”." In International Conference on Engineering, Technology and Social Science (ICONETOS 2020). Paris, France: Atlantis Press, 2021. http://dx.doi.org/10.2991/assehr.k.210421.090.
Повний текст джерелаLi, Ning, Zhidong Gao, and Xiaomei Qin. "The Theory and Application of Blended Learning." In 2017 International Conference on Economic Development and Education Management (ICEDEM 2017). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/icedem-17.2017.31.
Повний текст джерелаXing, Dr Eric. "Probabilistic Graphical Models-Theory, Algorithm, and Application." In Sixth International Conference on Machine Learning and Applications (ICMLA 2007). IEEE, 2007. http://dx.doi.org/10.1109/icmla.2007.126.
Повний текст джерелаObonyo, Stephen, and Daniel Ruiru. "Multitask Learning or Transfer Learning? Application to Cancer Detection." In 11th International Conference on Neural Computation Theory and Applications. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0008495805480555.
Повний текст джерелаJin-gang, Jiang, Sui Xiu-lin, Yang Wei, Lv Ning, and Zhang Jian-yi. "Application of TRIZ theory in problem based learning." In 2015 10th International Conference on Computer Science & Education (ICCSE). IEEE, 2015. http://dx.doi.org/10.1109/iccse.2015.7250378.
Повний текст джерелаZhang, Xiaoying, Hong Xie, and John C. S. Lui. "Heterogeneous Information Assisted Bandit Learning: Theory and Application." In 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2021. http://dx.doi.org/10.1109/icde51399.2021.00213.
Повний текст джерелаWen, Ping. "Application of Bruner's Learning Theory in Mathematics Studies." In Proceedings of the International Conference on Contemporary Education, Social Sciences and Ecological Studies (CESSES 2018). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/cesses-18.2018.53.
Повний текст джерелаVolchansky, Nadia. "CONSIDERATIONS FOR TEACHING DIGITAL PLATFORMS: SKILLS, THEORY, OR APPLICATION?" In International Conference on Education and New Learning Technologies. IATED, 2016. http://dx.doi.org/10.21125/edulearn.2016.0335.
Повний текст джерелаWahyuningsih, Sapti, and Darmawan Satyananda. "The Level of Creative Thinking Skill in Graph Theory Application Course." In 2nd International Conference on Learning Innovation. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0008411403030307.
Повний текст джерелаЗвіти організацій з теми "Application of learning theory"
Kingsbury, Penelope. Analysis of Learning Organization Theories and their Application to Public Organizations. Fort Belvoir, VA: Defense Technical Information Center, April 1999. http://dx.doi.org/10.21236/ada364145.
Повний текст джерелаMarkova, Oksana, Serhiy Semerikov та Maiia Popel. СoCalc as a Learning Tool for Neural Network Simulation in the Special Course “Foundations of Mathematic Informatics”. Sun SITE Central Europe, травень 2018. http://dx.doi.org/10.31812/0564/2250.
Повний текст джерелаBabkin, Vladyslav V., Viktor V. Sharavara, Volodymyr V. Sharavara, Vladyslav V. Bilous, Andrei V. Voznyak, and Serhiy Ya Kharchenko. Using augmented reality in university education for future IT specialists: educational process and student research work. CEUR Workshop Proceedings, July 2021. http://dx.doi.org/10.31812/123456789/4632.
Повний текст джерелаIatsyshyn, Anna V., Valeriia O. Kovach, Yevhen O. Romanenko, and Andrii V. Iatsyshyn. Cloud services application ways for preparation of future PhD. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3248.
Повний текст джерелаNosenko, Yuliya H., Maiia V. Popel, and Mariya P. Shyshkina. The state of the art and perspectives of using adaptive cloud-based learning systems in higher education pedagogical institutions (the scope of Ukraine). [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3246.
Повний текст джерелаSAINI, RAVINDER, AbdulKhaliq Alshadid, and Lujain Aldosari. Investigation on the application of artificial intelligence in prosthodontics. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, December 2022. http://dx.doi.org/10.37766/inplasy2022.12.0096.
Повний текст джерелаIatsyshyn, Anna V., Valeriia O. Kovach, Volodymyr O. Lyubchak, Yurii O. Zuban, Andriy G. Piven, Oleksandra M. Sokolyuk, Andrii V. Iatsyshyn, Oleksandr O. Popov, Volodymyr O. Artemchuk, and Mariya P. Shyshkina. Application of augmented reality technologies for education projects preparation. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3856.
Повний текст джерелаKramarenko, Tetiana H., Olha S. Pylypenko, and Vladimir I. Zaselskiy. Prospects of using the augmented reality application in STEM-based Mathematics teaching. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3753.
Повний текст джерелаMONAKO, T. P. THE PANDEMIC AND THE EDUCATION SYSTEM. Science and Innovation Center Publishing House, 2021. http://dx.doi.org/10.12731/2658-4034-2021-12-2-2-79-84.
Повний текст джерелаKanivets, Oleksandr V., Irina М. Kanivets, Natalia V. Kononets, Tetyana М. Gorda, and Ekaterina O. Shmeltser. Development of mobile applications of augmented reality for projects with projection drawings. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3745.
Повний текст джерела