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Статті в журналах з теми "Big data training"
Минязова, Е. Р. ""Big Data" and personalized training." Higher education today, no. 5-6 (July 18, 2022): 41–45. http://dx.doi.org/10.18137/rnu.het.22.05-06.p.041.
Повний текст джерелаScaife, Anna M. M., and Sally E. Cooper. "The DARA Big Data Project." Proceedings of the International Astronomical Union 14, A30 (August 2018): 569. http://dx.doi.org/10.1017/s174392131900543x.
Повний текст джерелаAbdullateef Omitogun, Abdullateef Omitogun, and Khalid Al-Adeem Abdullateef Omitogun. "Auditors’ Perceptions of and Competencies in Big Data and Data Analytics: An Empirical Investigation." International Journal of Computer Auditing 1, no. 1 (December 2019): 092–113. http://dx.doi.org/10.53106/256299802019120101005.
Повний текст джерелаWang, Yiting, and Le Yu. "Multisource Analysis of Big Data Technology: Accessing Data Sources for Teacher Management of Sports Training Institutions." Mobile Information Systems 2022 (August 13, 2022): 1–12. http://dx.doi.org/10.1155/2022/5115184.
Повний текст джерелаWang, Huiqin. "College Physical Education and Training in Big Data: A Big Data Mining and Analysis System." Journal of Healthcare Engineering 2021 (November 30, 2021): 1–8. http://dx.doi.org/10.1155/2021/3585630.
Повний текст джерелаJinhui, Zheng, Wang Sheng, Zheng Jinhong, Cai Guoliang, Cai Zhiqiang, and Du Yuntao. "Analysis on Survey Data of Special Physical Training for Skiers in Summer Training Based on Big Data." Mobile Information Systems 2021 (December 28, 2021): 1–6. http://dx.doi.org/10.1155/2021/3024089.
Повний текст джерелаSerik, M., G. Nurbekova, and J. Kultan. "Big data technology in education." Bulletin of the Karaganda University. Pedagogy series 100, no. 4 (December 28, 2020): 8–15. http://dx.doi.org/10.31489/2020ped4/8-15.
Повний текст джерелаQu, Qingling, Meiling An, Jinqian Zhang, Ming Li, Kai Li, and Sukwon Kim. "Biomechanics and Neuromuscular Control Training in Table Tennis Training Based on Big Data." Contrast Media & Molecular Imaging 2022 (August 10, 2022): 1–10. http://dx.doi.org/10.1155/2022/3725295.
Повний текст джерелаLane, Julia. "BIG DATA: THE ROLE OF EDUCATION AND TRAINING." Journal of Policy Analysis and Management 35, no. 3 (May 10, 2016): 722–24. http://dx.doi.org/10.1002/pam.21922.
Повний текст джерелаWei, Jingwei. "Study and application of computer information big data in basketball vision system using high-definition camera motion data capture." Journal of Physics: Conference Series 2083, no. 4 (November 1, 2021): 042003. http://dx.doi.org/10.1088/1742-6596/2083/4/042003.
Повний текст джерелаДисертації з теми "Big data training"
Guo, Zhenyu. "Data famine in big data era : machine learning algorithms for visual object recognition with limited training data." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/46412.
Повний текст джерелаHmida, Hmida. "Extension des Programmes Génétiques pour l’apprentissage supervisé à partir de très larges Bases de Données (Big data)." Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLED047.
Повний текст джерелаIn this thesis, we investigate the adaptation of GP to overcome the data Volume hurdle in Big Data problems. GP is a well-established meta-heuristic for classification problems but is impaired with its computing cost. First, we conduct an extensive review enriched with an experimental comparative study of training set sampling algorithms used for GP. Then, based on the previous study results, we propose some extensions based on hierarchical sampling. The latter combines active sampling algorithms on several levels and has proven to be an appropriate solution for sampling techniques that can’t deal with large datatsets (like TBS) and for applying GP to a Big Data problem as Higgs Boson classification.Moreover, we formulate a new sampling approach called “adaptive sampling”, based on controlling sampling frequency depending on learning process and through fixed, determinist and adaptive control schemes. Finally, we present how an existing GP implementation (DEAP) can be adapted by distributing evaluations on a Spark cluster. Then, we demonstrate how this implementation can be run on tiny clusters by sampling.Experiments show the great benefits of using Spark as parallelization technology for GP
張金慶 and Kam-hing Cheung. "Quality training: an expert system application." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1996. http://hub.hku.hk/bib/B31267038.
Повний текст джерелаVarga, Tamás. "Off-line cursive handwriting recognition using synthetic training data." Berlin Aka, 2006. http://deposit.d-nb.de/cgi-bin/dokserv?id=2838183&prov=M&dok_var=1&dok_ext=htm.
Повний текст джерелаLyttkens, Peter. "Electromagnetic field and neurological disorders Alzheimer´s disease, why the problem is difficult and how to solve it." Thesis, Uppsala universitet, Logopedi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-380074.
Повний текст джерелаGrard, Matthieu. "Generic instance segmentation for object-oriented bin-picking." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEC015.
Повний текст джерелаReferred to as robotic random bin-picking, a fast-expanding industrial task consists in robotizing the unloading of many object instances piled up in bulk, one at a time, for further processing such as kitting or part assembling. However, explicit object models are not always available in many bin-picking applications, especially in the food and automotive industries. Furthermore, object instances are often subject to intra-class variations, for example due to elastic deformations.Object pose estimation techniques, which require an explicit model and assume rigid transformations, are therefore not suitable in such contexts. The alternative approach, which consists in detecting grasps without an explicit notion of object, proves hardly efficient when the object geometry makes bulk instances prone to occlusion and entanglement. These approaches also typically rely on a multi-view scene reconstruction that may be unfeasible due to transparent and shiny textures, or that reduces critically the time frame for image processing in high-throughput robotic applications.In collaboration with Siléane, a French company in industrial robotics, we thus aim at developing a learning-based solution for localizing the most affordable instance of a pile from a single image, in open loop, without explicit object models. In the context of industrial bin-picking, our contribution is two-fold.First, we propose a novel fully convolutional network (FCN) for jointly delineating instances and inferring the spatial layout at their boundaries. Indeed, the state-of-the-art methods for such a task rely on two independent streams for boundaries and occlusions respectively, whereas occlusions often cause boundaries. Specifically, the mainstream approach, which consists in isolating instances in boxes before detecting boundaries and occlusions, fails in bin-picking scenarios as a rectangle region often includes several instances. By contrast, our box proposal-free architecture recovers fine instance boundaries, augmented with their occluding side, from a unified scene representation. As a result, the proposed network outperforms the two-stream baselines on synthetic data and public real-world datasets.Second, as FCNs require large training datasets that are not available in bin-picking applications, we propose a simulation-based pipeline for generating training images using physics and rendering engines. Specifically, piles of instances are simulated and rendered with their ground-truth annotations from sets of texture images and meshes to which multiple random deformations are applied. We show that the proposed synthetic data is plausible for real-world applications in the sense that it enables the learning of deep representations transferable to real data. Through extensive experiments on a real-world robotic setup, our synthetically trained network outperforms the industrial baseline while achieving real-time performances. The proposed approach thus establishes a new baseline for model-free object-oriented bin-picking
Narmack, 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.
Повний текст джерелаMorgondagens 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.
TSAI, YU-SHUN, and 蔡有順. "A Study on the Training Situation and Gap of Courses in Taiwan's Big Data." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/52913800576229564520.
Повний текст джерела中華大學
科技管理學系
105
In this era, the new technology applications has emerge in the advancement of technology. For example: Internet of Things(IOT), Autonomous Cars, Smart City, Artificial Intelligence(AI), Robot, Stem cells cultured in vitro, and Big Data. Application the new technologies was inevitable when the emerging technologies is growth, and the emerging technologies talents demand. However, the talents demand depends on the training programs. Big data courses as an example, many university and personnel training of institutions to established the big data courses, but this courses is diversified development. Therefore, this study is expected to compile a many of institutions to established the big data courses, and interviews with related experts, from the current understanding of the big data courses training situation and gap in Taiwan. This study collection of the 15 universities are courses planning of the big data courses, and big data information. In Taiwan, the big data courses to established the college of management, and set up the courses is data analysis and application. This courses set up in the department of statistics, mathematics, information management, and other fields. And the future the big data courses can be opened to more information processing courses in Taiwan. So that the amount of talented people is more valuable in Taiwan.
(5929568), Tommy Y. Chang. "Reducing Wide-Area Satellite Data to Concise Sets for More Efficient Training and Testing of Land-Cover Classifiers." Thesis, 2019.
Знайти повний текст джерелаКниги з теми "Big data training"
Big learning data. Alexandria, VA: ASTD Press, 2014.
Знайти повний текст джерелаVarlamov, Oleg. Mivar databases and rules. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.
Повний текст джерелаNimatulaev, Magomedhan. Information technology in professional activities. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1031122.
Повний текст джерелаVarlamov, Oleg. Fundamentals of creating MIVAR expert systems. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1513119.
Повний текст джерелаMasie, Elliott. Big Learning Data. American Society for Training & Development, 2013.
Знайти повний текст джерелаTraining Students to Extract Value from Big Data. Washington, D.C.: National Academies Press, 2014. http://dx.doi.org/10.17226/18981.
Повний текст джерелаTraining Students to Extract Value from Big Data: Summary of a Workshop. National Academies Press, 2015.
Знайти повний текст джерелаCommittee on Applied and Theoretical Statistics, National Research Council, Division on Engineering and Physical Sciences, Board on Mathematical Sciences and Their Applications, and Maureen Mellody. Training Students to Extract Value from Big Data: Summary of a Workshop. National Academies Press, 2015.
Знайти повний текст джерелаCommittee on Applied and Theoretical Statistics, National Research Council, Division on Engineering and Physical Sciences, Board on Mathematical Sciences and Their Applications, and Maureen Mellody. Training Students to Extract Value from Big Data: Summary of a Workshop. National Academies Press, 2015.
Знайти повний текст джерелаCommittee on Applied and Theoretical Statistics, National Research Council, Division on Engineering and Physical Sciences, Board on Mathematical Sciences and Their Applications, and Maureen Mellody. Training Students to Extract Value from Big Data: Summary of a Workshop. National Academies Press, 2015.
Знайти повний текст джерелаЧастини книг з теми "Big data training"
Schroth, Stephen T. "Education and Training." In Encyclopedia of Big Data, 430–33. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-32010-6_81.
Повний текст джерелаSchroth, Stephen T. "Education and Training." In Encyclopedia of Big Data, 1–4. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-32001-4_81-1.
Повний текст джерелаSu, Man-Na, Zhi-Jian Fang, Shao-Zhen Ye, Ying-Jie Wu, and Yang-Geng Fu. "An Optimized Artificial Bee Colony Based Parameter Training Method for Belief Rule-Base." In Big Data, 77–93. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2922-7_5.
Повний текст джерелаWani, M. Arif, Farooq Ahmad Bhat, Saduf Afzal, and Asif Iqbal Khan. "Training Supervised Deep Learning Networks." In Studies in Big Data, 31–52. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6794-6_3.
Повний текст джерелаKumar, Aditya, and Satish Narayana Srirama. "Fog Enabled Distributed Training Architecture for Federated Learning." In Big Data Analytics, 78–92. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93620-4_7.
Повний текст джерелаQu, Zhaowei, Chunye Wu, Xiaoru Wang, and Yanjiao Zhao. "Identification of Sentiment Labels Based on Self-training." In Data Mining and Big Data, 404–13. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93803-5_38.
Повний текст джерелаLiu, Ruyi, Yi Zhang, Damon M. Chandler, Qiguang Miao, and Tiange Liu. "LaG-DESIQUE: A Local-and-Global Blind Image Quality Evaluator Without Training on Human Opinion Scores." In Big Data, 268–77. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2922-7_18.
Повний текст джерелаJia, Xue-peng, and Xiao-feng Rong. "A Self-training Method for Detection of Phishing Websites." In Data Mining and Big Data, 414–25. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93803-5_39.
Повний текст джерелаZhao, Jiaqi, Ting Bai, Yuting Wei, and Bin Wu. "PoetryBERT: Pre-training with Sememe Knowledge for Classical Chinese Poetry." In Data Mining and Big Data, 369–84. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-8991-9_26.
Повний текст джерелаWang, Youyun, Chuzhe Tang, and Xujia Yao. "A Distribution-Aware Training Scheme for Learned Indexes." In Web and Big Data, 143–57. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85899-5_11.
Повний текст джерелаТези доповідей конференцій з теми "Big data training"
Giobergia, Flavio, and Elena Baralis. "Fast Self-Organizing Maps Training." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006055.
Повний текст джерелаWang, Fei, Guoyang Chen, Weifeng Zhang, and Tiark Rompf. "Parallel Training via Computation Graph Transformation." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006180.
Повний текст джерелаKhan, Rituparna, and Michael Gubanov. "Towards Tabular Embeddings, Training the Relational Models." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9377769.
Повний текст джерелаHu, Ziqing, Yihao Fang, and Lizhen Lin. "Training Graph Neural Networks by Graphon Estimation." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671996.
Повний текст джерелаVan, Minh-Hao, Wei Du, Xintao Wu, Feng Chen, and Aidong Lu. "Defending Evasion Attacks via Adversarially Adaptive Training." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020474.
Повний текст джерелаPeng, Zhanglin, Jiamin Ren, Ruimao Zhang, Lingyun Wu, Xinjiang Wang, and Ping Luo. "Scheduling Large-scale Distributed Training via Reinforcement Learning." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622264.
Повний текст джерелаShah, Ruchi, Shaoshuai Zhang, Ying Lin, and Panruo Wu. "xSVM: Scalable Distributed Kernel Support Vector Machine Training." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006315.
Повний текст джерелаJeong, Jueun, Hanseok Jeong, and Han-Joon Kim. "An AutoEncoder-based Numerical Training Data Augmentation Technique." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020487.
Повний текст джерелаKhan, Rituparna, and Michael Gubanov. "WebLens: Towards Web-scale Data Integration, Training the Models." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9377742.
Повний текст джерелаTripathi, Samarth, Jiayi Liu, Sauptik Dhar, Unmesh Kurup, and Mohak Shah. "Improving Model Training by Periodic Sampling over Weight Distributions." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378212.
Повний текст джерелаЗвіти організацій з теми "Big data training"
Adebayo, Oliver, Joanna Aldoori, William Allum, Noel Aruparayil, Abdul Badran, Jasmine Winter Beatty, Sanchita Bhatia, et al. Future of Surgery: Technology Enhanced Surgical Training: Report of the FOS:TEST Commission. The Royal College of Surgeons of England, August 2022. http://dx.doi.org/10.1308/fos2.2022.
Повний текст джерелаGuérin, Laurence, Patrick Sins, Lida Klaver, and Juliette Walma van der Molen. Onderzoeksrapport Samen werken aan Bèta Burgerschap. Saxion, 2021. http://dx.doi.org/10.14261/ff0c6282-93e2-41a7-b60ab9bceb2a4328.
Повний текст джерелаLI, Zhendong, Hangjian Qiu, xiaoqian Wang, chengcheng Zhang, and Yuejuan Zhang. Comparative Efficacy of 5 non-pharmaceutical Therapies For Adults With Post-stroke Cognitive Impairment: Protocol For A Bayesian Network Analysis Based on 55 Randomized Controlled Trials. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, June 2022. http://dx.doi.org/10.37766/inplasy2022.6.0036.
Повний текст джерелаAfrican Open Science Platform Part 1: Landscape Study. Academy of Science of South Africa (ASSAf), 2019. http://dx.doi.org/10.17159/assaf.2019/0047.
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