Literatura científica selecionada sobre o tema "Small datasets"
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Artigos de revistas sobre o assunto "Small datasets"
Agliari, Elena, Francesco Alemanno, Miriam Aquaro, Adriano Barra, Fabrizio Durante e Ido Kanter. "Hebbian dreaming for small datasets". Neural Networks 173 (maio de 2024): 106174. http://dx.doi.org/10.1016/j.neunet.2024.106174.
Texto completo da fonteIngrassia, Salvatore, e Isabella Morlini. "Neural Network Modeling for Small Datasets". Technometrics 47, n.º 3 (agosto de 2005): 297–311. http://dx.doi.org/10.1198/004017005000000058.
Texto completo da fonteRicchiuto, Piero, Judy C. G. Sng e Wilson Wen Bin Goh. "Analysing extremely small sized ratio datasets". International Journal of Bioinformatics Research and Applications 11, n.º 3 (2015): 268. http://dx.doi.org/10.1504/ijbra.2015.069225.
Texto completo da fonteTuomo, Alasalmi, Jaakko Suutala, Juha Röning e Heli Koskimäki. "Better Classifier Calibration for Small Datasets". ACM Transactions on Knowledge Discovery from Data 14, n.º 3 (14 de maio de 2020): 1–19. http://dx.doi.org/10.1145/3385656.
Texto completo da fonteMontalvão, J., R. Attux e D. G. Silva. "Simple entropy estimator for small datasets". Electronics Letters 48, n.º 17 (16 de agosto de 2012): 1059–61. http://dx.doi.org/10.1049/el.2012.2002.
Texto completo da fonteKhobragade, Vandana, M. S. Pradeep Kumar Patnaik e Srinivasa Rao Sura. "Revaluating Pretraining in Small Size Training Sample Regime". International Journal of Electrical and Electronics Research 10, n.º 3 (30 de setembro de 2022): 694–704. http://dx.doi.org/10.37391/ijeer.100346.
Texto completo da fonteBurmakova, Anastasiya, e Diana Kalibatienė. "Applying Fuzzy Inference and Machine Learning Methods for Prediction with a Small Dataset: A Case Study for Predicting the Consequences of Oil Spills on a Ground Environment". Applied Sciences 12, n.º 16 (18 de agosto de 2022): 8252. http://dx.doi.org/10.3390/app12168252.
Texto completo da fonteJamjoom, Mona. "The pertinent single-attribute-based classifier for small datasets classification". International Journal of Electrical and Computer Engineering (IJECE) 10, n.º 3 (1 de junho de 2020): 3227. http://dx.doi.org/10.11591/ijece.v10i3.pp3227-3234.
Texto completo da fontePetráš, Jaroslav, Marek Pavlík, Ján Zbojovský, Ardian Hyseni e Jozef Dudiak. "Benford’s Law in Electric Distribution Network". Mathematics 11, n.º 18 (10 de setembro de 2023): 3863. http://dx.doi.org/10.3390/math11183863.
Texto completo da fonteAndonie, Răzvan. "Extreme Data Mining: Inference from Small Datasets". International Journal of Computers Communications & Control 5, n.º 3 (1 de setembro de 2010): 280. http://dx.doi.org/10.15837/ijccc.2010.3.2481.
Texto completo da fonteTeses / dissertações sobre o assunto "Small datasets"
Shi, Xiaojin. "Visual learning from small training datasets /". Diss., Digital Dissertations Database. Restricted to UC campuses, 2005. http://uclibs.org/PID/11984.
Texto completo da fonteVan, Koten Chikako, e n/a. "Bayesian statistical models for predicting software effort using small datasets". University of Otago. Department of Information Science, 2007. http://adt.otago.ac.nz./public/adt-NZDU20071009.120134.
Texto completo da fonteZhao, Amy(Xiaoyu Amy). "Learning distributions of transformations from small datasets for applied image synthesis". Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/128342.
Texto completo da fonteCataloged from PDF of thesis. "February 2020."
Includes bibliographical references (pages 75-91).
Much of the recent research in machine learning and computer vision focuses on applications with large labeled datasets. However, in realistic settings, it is much more common to work with limited data. In this thesis, we investigate two applications of image synthesis using small datasets. First, we demonstrate how to use image synthesis to perform data augmentation, enabling the use of supervised learning methods with limited labeled data. Data augmentation -- typically the application of simple, hand-designed transformations such as rotation and scaling -- is often used to expand small datasets. We present a method for learning complex data augmentation transformations, producing examples that are more diverse, realistic, and useful for training supervised systems than hand-engineered augmentation. We demonstrate our proposed augmentation method for improving few-shot object classification performance, using a new dataset of collectible cards with fine-grained differences. We also apply our method to medical image segmentation, enabling the training of a supervised segmentation system using just a single labeled example. In our second application, we present a novel image synthesis task: synthesizing time lapse videos of the creation of digital and watercolor paintings. Using a recurrent model of paint strokes and a novel training scheme, we create videos that tell a plausible visual story of the painting process.
by Amy (Xiaoyu) Zhao.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Arzamasov, Vadim [Verfasser], e K. [Akademischer Betreuer] Böhm. "Comprehensible and Robust Knowledge Discovery from Small Datasets / Vadim Arzamasov ; Betreuer: K. Böhm". Karlsruhe : KIT-Bibliothek, 2021. http://d-nb.info/1238148166/34.
Texto completo da fonteLazarovici, Allan 1979. "Development of gene-finding algorithms for fungal genomes : dealing with small datasets and leveraging comparative genomics". Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/29681.
Texto completo da fonteIncludes bibliographical references (leaves 60-62).
A computer program called FUNSCAN was developed which identifies protein coding regions in fungal genomes. Gene structural and compositional properties are modeled using a Hidden Markov Model. Separate training and testing sets for FUNSCAN were obtained by aligning cDNAs from an organism to their genomic loci, generating a 'gold standard' set of annotated genes. The performance of FUNSCAN is competitive with other computer programs design to identify protein coding regions in fungal genomes. A technique called 'Training Set Augmentation' is described which can be used to train FUNSCAN when only a small training set of genes is available. Techniques that combine alignment algorithms with FUNSCAN to identify novel genes are also discussed and explored.
by Allan Lazarovici.
M.Eng.and S.B.
Horečný, Peter. "Metody segmentace obrazu s malými trénovacími množinami". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-412996.
Texto completo da fonteLucy, Caleb O. "Rapid Acquisition of Low Cost High-Resolution Elevation Datasets Using a Small Unmanned Aircraft System: An Application for Measuring River Geomorphic Change". Thesis, Boston College, 2015. http://hdl.handle.net/2345/bc-ir:104880.
Texto completo da fonteEmerging methods for acquiring high-resolution topographic datasets have the potential to open new opportunities for quantitative geomorphic analysis. This study demonstrates a technique for rapidly obtaining structure from motion (SfM) photogrammetry-derived digital elevation models (DEMs) using aerial photographs acquired with a small unmanned aircraft system (sUAS). In conjunction with collection of aerial imagery, study sites are surveyed with a differential global position system (dGPS)-enabled total station (TPS) for georeferencing and accuracy assessment of sUAS SfM measurements. Results from sUAS SfM surveys of upland river channels in northern New England consistently produce DEMs and orthoimagery with ~1 cm pixel resolution. One-to-one point measurement comparisons demonstrate sUAS SfM systematically measures elevations about 0.16 ±0.23 m higher than TPS equivalents (0.28 m RMSE). Bathymetric (i.e. submerged or subaqueous) sUAS SfM measurements are 0.20 ±0.24 m (0.31 m RMSE) higher than TPS, whereas exposed (subaerial) points are 0.14 ±0.22 m (0.26 m RMSE) higher than TPS. Serial comparison of DEMs obtained before and after a two-year flood event indicates cut bank erosion and point bar deposition of ~0.10 m, consistent with expectations for channel evolution. DEMs acquired with the sUAS SfM are of comparable resolution but a lower cost alternative to those from airborne light detection and ranging (lidar), the current standard for topographic imagery. Furthermore, lidar is not available for much of the United States and sUAS SfM provides an efficient means for expanding coverage of this critical elevation dataset. Due to their utility in municipal, land use, and emergency planning, the demand for high-resolution topographic datasets continues to increase among governments, research institutions, and private sector consulting firms. Terrain analysis using sUAS SfM could therefore be a boon to river management and restoration in northern New England and other regions
Thesis (MS) — Boston College, 2015
Submitted to: Boston College. Graduate School of Arts and Sciences
Discipline: Geology and Geophysics
Oppon, Ekow CruickShank. "Synergistic use of promoter prediction algorithms: a choice of small training dataset?" Thesis, University of the Western Cape, 2000. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_8222_1185436339.
Texto completo da fontePromoter detection, especially in prokaryotes, has always been an uphill task and may remain so, because of the many varieties of sigma factors employed by various organisms in transcription. The situation is made more complex by the fact, that any seemingly unimportant sequence segment may be turned into a promoter sequence by an activator or repressor (if the actual promoter sequence is made unavailable). Nevertheless, a computational approach to promoter detection has to be performed due to number of reasons. The obvious that comes to mind is the long and tedious process involved in elucidating promoters in the &lsquo
wet&rsquo
laboratories not to mention the financial aspect of such endeavors. Promoter detection/prediction of an organism with few characterized promoters (M.tuberculosis) as envisaged at the beginning of this work was never going to be easy. Even for the few known Mycobacterial promoters, most of the respective sigma factors associated with their transcription were not known. If the information (promoter-sigma) were available, the research would have been focused on categorizing the promoters according to sigma factors and training the methods on the respective categories. That is assuming that, there would be enough training data for the respective categories. Most promoter detection/prediction studies have been carried out on E.coli because of the availability of a number of experimentally characterized promoters (+- 310). Even then, no researcher to date has extended the research to the entire E.coli genome.
Forsberg, Fredrik, e Gonzalez Pierre Alvarez. "Unsupervised Machine Learning: An Investigation of Clustering Algorithms on a Small Dataset". Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-16300.
Texto completo da fonteGay, Antonin. "Pronostic de défaillance basé sur les données pour la prise de décision en maintenance : Exploitation du principe d'augmentation de données avec intégration de connaissances à priori pour faire face aux problématiques du small data set". Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0059.
Texto completo da fonteThis CIFRE PhD is a joint project between ArcelorMittal and the CRAN laboratory, with theaim to optimize industrial maintenance decision-making through the exploitation of the available sources of information, i.e. industrial data and knowledge, under the industrial constraints presented by the steel-making context. Current maintenance strategy on steel lines is based on regular preventive maintenance. Evolution of preventive maintenance towards a dynamic strategy is done through predictive maintenance. Predictive maintenance has been formalized within the Prognostics and Health Management (PHM) paradigm as a seven steps process. Among these PHM steps, this PhD's work focuses on decision-making and prognostics. The Industry 4.0 context put emphasis on data-driven approaches, which require large amount of data that industrial systems cannot ystematically supply. The first contribution of the PhD consists in proposing an equation to link prognostics performances to the number of available training samples. This contribution allows to predict prognostics performances that could be obtained with additional data when dealing with small datasets. The second contribution of the PhD focuses on evaluating and analyzing the performance of data augmentation when applied to rognostics on small datasets. Data augmentation leads to an improvement of prognostics performance up to 10%. The third contribution of the PhD consists in the integration of expert knowledge into data augmentation. Statistical knowledge integration proved efficient to avoid performance degradation caused by data augmentation under some unfavorable conditions. Finally, the fourth contribution consists in the integration of prognostics in maintenance decision-making cost modeling and the evaluation of prognostics impact on maintenance decision cost. It demonstrates that (i) the implementation of predictive maintenance reduces maintenance cost up to 18-20% and ii) the 10% prognostics improvement can reduce maintenance cost by an additional 1%
Livros sobre o assunto "Small datasets"
Sarang, Poornachandra. Clustering Small to Humongous Datasets. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49094-1.
Texto completo da fonteMachine Learning Methods with Noisy, Incomplete or Small Datasets. MDPI, 2021. http://dx.doi.org/10.3390/books978-3-0365-1288-4.
Texto completo da fonteSchoot, Rens van de, e Milica Miočević. Small Sample Size Solutions. Taylor & Francis Group, 2020.
Encontre o texto completo da fonteSchoot, Rens van de, e Milica Miočević. Small Sample Size Solutions: A How to Guide for Applied Researchers and Practitioners. Taylor & Francis Group, 2020.
Encontre o texto completo da fonteSchoot, Rens van de, e Milica Miočević. Small Sample Size Solutions: A How to Guide for Applied Researchers and Practitioners. Taylor & Francis Group, 2020.
Encontre o texto completo da fonteSchoot, Rens van de, e Milica Miočević. Small Sample Size Solutions: A How to Guide for Applied Researchers and Practitioners. Taylor & Francis Group, 2020.
Encontre o texto completo da fonteSchoot, Rens van de, e Milica Miočević. Small Sample Size Solutions: A How to Guide for Applied Researchers and Practitioners. Taylor & Francis Group, 2020.
Encontre o texto completo da fonteWoldu, Gabriel Temesgen. Do fiscal regimes matter for fiscal sustainability in South Africa? A Markov-switching approach. UNU-WIDER, 2020. http://dx.doi.org/10.35188/unu-wider/2020/920-4.
Texto completo da fonteTyrkkö, Jukka. Discovering the Past for Yourself. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190611040.003.0012.
Texto completo da fonteLewis, Oliver. Council of Europe. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198786627.003.0004.
Texto completo da fonteCapítulos de livros sobre o assunto "Small datasets"
Tulp, Jan Willem. "Designing for Small and Large Datasets". In New Challenges for Data Design, 377–90. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-6596-5_20.
Texto completo da fonteCadenas, José M., M. Carmen Garrido e Raquel Martínez. "Fuzzy Discretization Process from Small Datasets". In Studies in Computational Intelligence, 263–79. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23392-5_15.
Texto completo da fonteIbrikci, Turgay, Esra Mahsereci Karabulut e Jean Dieu Uwisengeyimana. "Meta Learning on Small Biomedical Datasets". In Lecture Notes in Electrical Engineering, 933–39. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0557-2_89.
Texto completo da fonteRaul, Nataasha, Royston D’mello e Mandar Bhalerao. "Keystroke Dynamics Authentication Using Small Datasets". In Communications in Computer and Information Science, 89–96. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7561-3_7.
Texto completo da fonteDash, Amanda, e Alexandra Branzan Albu. "Texture-Based Data Augmentation for Small Datasets". In Advanced Concepts for Intelligent Vision Systems, 345–56. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45382-3_29.
Texto completo da fonteRaj, Akhilesh, Kanishk Gandhi, Bhanu Teja Nalla e Nishchal K. Verma. "Object Detection and Recognition Using Small Labeled Datasets". In Advances in Intelligent Systems and Computing, 407–19. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1135-2_31.
Texto completo da fonteBashar, Md Abul, Richi Nayak, Nicolas Suzor e Bridget Weir. "Misogynistic Tweet Detection: Modelling CNN with Small Datasets". In Communications in Computer and Information Science, 3–16. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6661-1_1.
Texto completo da fonteRacca, Alberto, e Luca Magri. "Statistical Prediction of Extreme Events from Small Datasets". In Computational Science – ICCS 2022, 707–13. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08757-8_58.
Texto completo da fonteTato, Ange, e Roger Nkambou. "Deep Knowledge Tracing on Skills with Small Datasets". In Intelligent Tutoring Systems, 123–35. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09680-8_12.
Texto completo da fonteRuiz, Victoria, Ángel Sánchez, José F. Vélez e Bogdan Raducanu. "Waste Classification with Small Datasets and Limited Resources". In Intelligent Systems Reference Library, 185–203. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06307-7_10.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Small datasets"
Hiruta, Komei, Ryusuke Saito, Taro Hatakeyama, Atsushi Hashimoto e Satoshi Kurihara. "Conditional GAN for Small Datasets". In 2022 IEEE International Symposium on Multimedia (ISM). IEEE, 2022. http://dx.doi.org/10.1109/ism55400.2022.00062.
Texto completo da fonteSteedman, Mark, Miles Osborne, Anoop Sarkar, Stephen Clark, Rebecca Hwa, Julia Hockenmaier, Paul Ruhlen, Steven Baker e Jeremiah Crim. "Bootstrapping statistical parsers from small datasets". In the tenth conference. Morristown, NJ, USA: Association for Computational Linguistics, 2003. http://dx.doi.org/10.3115/1067807.1067851.
Texto completo da fonteXu, Peng, Dhruv Kumar, Wei Yang, Wenjie Zi, Keyi Tang, Chenyang Huang, Jackie Chi Kit Cheung, Simon J. D. Prince e Yanshuai Cao. "Optimizing Deeper Transformers on Small Datasets". In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.acl-long.163.
Texto completo da fonteNdipenoch, Nchongmaje, Alina Miron, Zidong Wang e Yongmin Li. "Retinal Image Segmentation with Small Datasets". In 10th International Conference on Bioimaging. SCITEPRESS - Science and Technology Publications, 2023. http://dx.doi.org/10.5220/0011779200003414.
Texto completo da fonteWang, Jingjie, Xiang Wei, Siyang Lu, Mingquan Wang, Xiaoyu Liu e Wei Lu. "Redesign Visual Transformer For Small Datasets". In 2022 IEEE Smartworld, Ubiquitous Intelligence & Computing, Scalable Computing & Communications, Digital Twin, Privacy Computing, Metaverse, Autonomous & Trusted Vehicles (SmartWorld/UIC/ScalCom/DigitalTwin/PriComp/Meta). IEEE, 2022. http://dx.doi.org/10.1109/smartworld-uic-atc-scalcom-digitaltwin-pricomp-metaverse56740.2022.00077.
Texto completo da fonteGao, Haoqi, e Koichi Ogawara. "Face alignment by learning from small real datasets and large synthetic datasets". In 2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML). IEEE, 2022. http://dx.doi.org/10.1109/cacml55074.2022.00073.
Texto completo da fonteXiao, Yabo, Shuai Guo, Tianqi Lv e Lei Jin. "Target Detection on Small Sample Specific Datasets". In 2018 Eighth International Conference on Instrumentation & Measurement, Computer, Communication and Control (IMCCC). IEEE, 2018. http://dx.doi.org/10.1109/imccc.2018.00332.
Texto completo da fonteYin, Mingjun, Zhiyong Chang e Yan Wang. "Adaptive Hybrid Vision Transformer for Small Datasets". In 2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2023. http://dx.doi.org/10.1109/ictai59109.2023.00132.
Texto completo da fonteLiu, Yanzhu, Adams Wai Kin Kong e Chi Keong Goh. "Deep Ordinal Regression Based on Data Relationship for Small Datasets". In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/330.
Texto completo da fonteAshrafi, Parivash, Yi Sun, Neil Davey, Rod Adams, Marc B. Brown, Maria Prapopoulou e Gary Moss. "The importance of hyperparameters selection within small datasets". In 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. http://dx.doi.org/10.1109/ijcnn.2015.7280645.
Texto completo da fonteRelatórios de organizações sobre o assunto "Small datasets"
Fraley, Chris, Adrian Raftery e Ron Wehrensy. Incremental Model-Based Clustering for Large Datasets With Small Clusters. Fort Belvoir, VA: Defense Technical Information Center, dezembro de 2003. http://dx.doi.org/10.21236/ada459790.
Texto completo da fonteChahal, Husanjot, Helen Toner e Ilya Rahkovsky. Small Data's Big AI Potential. Center for Security and Emerging Technology, setembro de 2021. http://dx.doi.org/10.51593/20200075.
Texto completo da fonteKurmann, André, Étienne Lalé e Lien Ta. Measuring Small Business Dynamics and Employment with Private-Sector Real-Time Data. CIRANO, agosto de 2022. http://dx.doi.org/10.54932/xsph3669.
Texto completo da fonteSalter, R., Quyen Dong, Cody Coleman, Maria Seale, Alicia Ruvinsky, LaKenya Walker e W. Bond. Data Lake Ecosystem Workflow. Engineer Research and Development Center (U.S.), abril de 2021. http://dx.doi.org/10.21079/11681/40203.
Texto completo da fonteHammouti, A., S. Larmagnat, C. Rivard e D. Pham Van Bang. Use of CT-scan images to build geomaterial 3D pore network representation in preparation for numerical simulations of fluid flow and heat transfer, Quebec. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331502.
Texto completo da fonteLers, Amnon, e Pamela J. Green. Analysis of Small RNAs Associated with Plant Senescence. United States Department of Agriculture, março de 2013. http://dx.doi.org/10.32747/2013.7593393.bard.
Texto completo da fontePuttanapong, Nattapong, Arturo M. Martinez Jr, Mildred Addawe, Joseph Bulan, Ron Lester Durante e Marymell Martillan. Predicting Poverty Using Geospatial Data in Thailand. Asian Development Bank, dezembro de 2020. http://dx.doi.org/10.22617/wps200434-2.
Texto completo da fonteTennant, David. Business Surveys on the Impact of COVID-19 on Jamaican Firms. Inter-American Development Bank, maio de 2021. http://dx.doi.org/10.18235/0003251.
Texto completo da fonteRenaud, Alexander, Michael Forte, Nicholas Spore, Brittany Bruder, Katherine Brodie, Jessamin Straub e Jeffrey Ruby. Evaluation of Unmanned Aircraft Systems for flood risk management : results of terrain and structure assessments. Engineer Research and Development Center (U.S.), agosto de 2022. http://dx.doi.org/10.21079/11681/45000.
Texto completo da fonteRuby, Jeffrey, Richard Massaro, John Anderson e Robert Fischer. Three-dimensional geospatial product generation from tactical sources, co-registration assessment, and considerations. Engineer Research and Development Center (U.S.), fevereiro de 2023. http://dx.doi.org/10.21079/11681/46442.
Texto completo da fonte