Artículos de revistas sobre el tema "Small datasets"
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Agliari, Elena, Francesco Alemanno, Miriam Aquaro, Adriano Barra, Fabrizio Durante y Ido Kanter. "Hebbian dreaming for small datasets". Neural Networks 173 (mayo de 2024): 106174. http://dx.doi.org/10.1016/j.neunet.2024.106174.
Texto completoIngrassia, Salvatore y 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 completoRicchiuto, Piero, Judy C. G. Sng y 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 completoTuomo, Alasalmi, Jaakko Suutala, Juha Röning y Heli Koskimäki. "Better Classifier Calibration for Small Datasets". ACM Transactions on Knowledge Discovery from Data 14, n.º 3 (14 de mayo de 2020): 1–19. http://dx.doi.org/10.1145/3385656.
Texto completoMontalvão, J., R. Attux y 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 completoKhobragade, Vandana, M. S. Pradeep Kumar Patnaik y Srinivasa Rao Sura. "Revaluating Pretraining in Small Size Training Sample Regime". International Journal of Electrical and Electronics Research 10, n.º 3 (30 de septiembre de 2022): 694–704. http://dx.doi.org/10.37391/ijeer.100346.
Texto completoBurmakova, Anastasiya y 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 completoJamjoom, 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 junio de 2020): 3227. http://dx.doi.org/10.11591/ijece.v10i3.pp3227-3234.
Texto completoPetráš, Jaroslav, Marek Pavlík, Ján Zbojovský, Ardian Hyseni y Jozef Dudiak. "Benford’s Law in Electric Distribution Network". Mathematics 11, n.º 18 (10 de septiembre de 2023): 3863. http://dx.doi.org/10.3390/math11183863.
Texto completoAndonie, Răzvan. "Extreme Data Mining: Inference from Small Datasets". International Journal of Computers Communications & Control 5, n.º 3 (1 de septiembre de 2010): 280. http://dx.doi.org/10.15837/ijccc.2010.3.2481.
Texto completoKu, C. J. y T. L. Fine. "A Bayesian Independence Test for Small Datasets". IEEE Transactions on Signal Processing 54, n.º 10 (octubre de 2006): 4026–31. http://dx.doi.org/10.1109/tsp.2006.880243.
Texto completoLi, Der-Chiang, Hung-Yu Chen y Qi-Shi Shi. "Learning from small datasets containing nominal attributes". Neurocomputing 291 (mayo de 2018): 226–36. http://dx.doi.org/10.1016/j.neucom.2018.02.069.
Texto completoXu, Weihuang, Guohao Yu, Alina Zare, Brendan Zurweller, Diane L. Rowland, Joel Reyes-Cabrera, Felix B. Fritschi, Roser Matamala y Thomas E. Juenger. "Overcoming small minirhizotron datasets using transfer learning". Computers and Electronics in Agriculture 175 (agosto de 2020): 105466. http://dx.doi.org/10.1016/j.compag.2020.105466.
Texto completoXu, Zi’an, Yin Dai, Fayu Liu, Weibing Chen, Yue Liu, Lifu Shi, Sheng Liu y Yuhang Zhou. "Swin MAE: Masked autoencoders for small datasets". Computers in Biology and Medicine 161 (julio de 2023): 107037. http://dx.doi.org/10.1016/j.compbiomed.2023.107037.
Texto completoBhalla, Vandna. "INNOVATIVE MODEL TO AUGMENT SMALL DATASETS FOR CLASSIFICATION". International Journal of Advanced Research 11, n.º 04 (30 de abril de 2023): 313–19. http://dx.doi.org/10.21474/ijar01/16658.
Texto completoKeum, Bitna, Juoh Sun, Woojin Lee, Seongheum Park y Harksoo Kim. "Persona-Identified Chatbot through Small-Scale Modeling and Data Transformation". Electronics 13, n.º 8 (9 de abril de 2024): 1409. http://dx.doi.org/10.3390/electronics13081409.
Texto completoBao, Yan, Frank Heilig, Chuo-Hsuan Lee y Edward J. Lusk. "Full Range Testing of the Small Size Effect Bias for Benford Screening: A Note". International Journal of Economics and Finance 10, n.º 6 (30 de abril de 2018): 47. http://dx.doi.org/10.5539/ijef.v10n6p47.
Texto completoSumalatha, M. y Latha Parthiban. "Augmentation of Predictive Competence of Non-Small Cell Lung Cancer Datasets through Feature Pre-Processing Techniques". EAI Endorsed Transactions on Pervasive Health and Technology 8, n.º 5 (2 de noviembre de 2022): e1. http://dx.doi.org/10.4108/eetpht.v8i5.3169.
Texto completoBai, Long, Liangyu Wang, Tong Chen, Yuanhao Zhao y Hongliang Ren. "Transformer-Based Disease Identification for Small-Scale Imbalanced Capsule Endoscopy Dataset". Electronics 11, n.º 17 (31 de agosto de 2022): 2747. http://dx.doi.org/10.3390/electronics11172747.
Texto completoBao, Yan, Chuo-Hsuan Lee, Frank Heilig y Edward J. Lusk. "Empirical Information on the Small Size Effect Bias Relative to the False Positive Rejection Error for Benford Test-Screening". International Journal of Economics and Finance 10, n.º 2 (3 de enero de 2018): 1. http://dx.doi.org/10.5539/ijef.v10n2p1.
Texto completoMabuni, D. y S. Aquter Babu. "High Accurate and a Variant of k-fold Cross Validation Technique for Predicting the Decision Tree Classifier Accuracy". International Journal of Innovative Technology and Exploring Engineering 10, n.º 2 (10 de enero de 2021): 105–10. http://dx.doi.org/10.35940/ijitee.c8403.0110321.
Texto completoJaryani, Farhang y Maryam Amiri. "A Pre-Trained Ensemble Model for Breast Cancer Grade Detection Based on Small Datasets". Iranian Journal of Health Sciences 11, n.º 1 (1 de enero de 2023): 47–58. http://dx.doi.org/10.32598/ijhs.11.1.883.1.
Texto completoKim, Dongseob, Seungho Lee, Junsuk Choe y Hyunjung Shim. "Weakly Supervised Semantic Segmentation for Driving Scenes". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 3 (24 de marzo de 2024): 2741–49. http://dx.doi.org/10.1609/aaai.v38i3.28053.
Texto completoXu, Xinkai, Hailan Zhang, Yan Ma, Kang Liu, Hong Bao y Xu Qian. "TranSDet: Toward Effective Transfer Learning for Small-Object Detection". Remote Sensing 15, n.º 14 (12 de julio de 2023): 3525. http://dx.doi.org/10.3390/rs15143525.
Texto completoDavila Delgado, Juan Manuel y Lukumon Oyedele. "Deep learning with small datasets: using autoencoders to address limited datasets in construction management". Applied Soft Computing 112 (noviembre de 2021): 107836. http://dx.doi.org/10.1016/j.asoc.2021.107836.
Texto completoMarston, Louise, Janet L. Peacock, Keming Yu, Peter Brocklehurst, Sandra A. Calvert, Anne Greenough y Neil Marlow. "Comparing methods of analysing datasets with small clusters: case studies using four paediatric datasets". Paediatric and Perinatal Epidemiology 23, n.º 4 (julio de 2009): 380–92. http://dx.doi.org/10.1111/j.1365-3016.2009.01046.x.
Texto completoKarunanithi, Sivarajan, Martin Simon y Marcel H. Schulz. "Automated analysis of small RNA datasets with RAPID". PeerJ 7 (10 de abril de 2019): e6710. http://dx.doi.org/10.7717/peerj.6710.
Texto completoGoyal, Gaurvi, Nicoletta Noceti y Francesca Odone. "Cross-view action recognition with small-scale datasets". Image and Vision Computing 120 (abril de 2022): 104403. http://dx.doi.org/10.1016/j.imavis.2022.104403.
Texto completoSingh, Gurpartap, Sunil Agrawal y Balwinder Singh Sohi. "Handwritten Gurmukhi Digit Recognition System for Small Datasets". Traitement du Signal 37, n.º 4 (10 de octubre de 2020): 661–69. http://dx.doi.org/10.18280/ts.370416.
Texto completoMauldin, Taylor, Anne H. Ngu, Vangelis Metsis y Marc E. Canby. "Ensemble Deep Learning on Wearables Using Small Datasets". ACM Transactions on Computing for Healthcare 2, n.º 1 (30 de diciembre de 2020): 1–30. http://dx.doi.org/10.1145/3428666.
Texto completoLi, Jingmei, Di Xue, Weifei Wu y Jiaxiang Wang. "Incremental Learning for Malware Classification in Small Datasets". Security and Communication Networks 2020 (20 de febrero de 2020): 1–12. http://dx.doi.org/10.1155/2020/6309243.
Texto completoBaroni, Michel, Fabrice Barthélémy y Mahdi Mokrane. "A repeat sales index robust to small datasets". Journal of Property Investment & Finance 29, n.º 1 (8 de febrero de 2011): 35–48. http://dx.doi.org/10.1108/14635781111100182.
Texto completovon Ungern-Sternberg, Britta S. y Adrian Regli. "Big problem, small incidence, and large registry datasets". Lancet Respiratory Medicine 4, n.º 1 (enero de 2016): 5–6. http://dx.doi.org/10.1016/s2213-2600(15)00519-6.
Texto completoVatian, A. S., A. A. Golubev, N. F. Gusarova, N. V. Dobrenko, A. A. Zubanenko, E. S. Kustova, A. A. Tatarinova, I. V. Tomilov y G. F. Shovkoplyas. "Intelligent clinical decision support for small patient datasets". Scientific and Technical Journal of Information Technologies, Mechanics and Optics 23, n.º 3 (1 de junio de 2023): 595–607. http://dx.doi.org/10.17586/2226-1494-2023-23-3-595-607.
Texto completoTanov, Vladislav. "Data-Centric Optimization Approach for Small, Imbalanced Datasets". Journal of information and organizational sciences 47, n.º 1 (30 de junio de 2023): 167–77. http://dx.doi.org/10.31341/jios.47.1.9.
Texto completoWu, Yumei, Jingxiu Yao, Shuo Chang y Bin Liu. "LIMCR: Less-Informative Majorities Cleaning Rule Based on Naïve Bayes for Imbalance Learning in Software Defect Prediction". Applied Sciences 10, n.º 23 (24 de noviembre de 2020): 8324. http://dx.doi.org/10.3390/app10238324.
Texto completoPerin, Vinicius, Samapriya Roy, Joe Kington, Thomas Harris, Mirela G. Tulbure, Noah Stone, Torben Barsballe, Michele Reba y Mary A. Yaeger. "Monitoring Small Water Bodies Using High Spatial and Temporal Resolution Analysis Ready Datasets". Remote Sensing 13, n.º 24 (20 de diciembre de 2021): 5176. http://dx.doi.org/10.3390/rs13245176.
Texto completoSheeny, Marcel, Andrew Wallace y Sen Wang. "RADIO: Parameterized Generative Radar Data Augmentation for Small Datasets". Applied Sciences 10, n.º 11 (2 de junio de 2020): 3861. http://dx.doi.org/10.3390/app10113861.
Texto completoLi, Jindi, Kefeng Li, Guangyuan Zhang, Jiaqi Wang, Keming Li y Yumin Yang. "Recognition of Dorsal Hand Vein in Small-Scale Sample Database Based on Fusion of ResNet and HOG Feature". Electronics 11, n.º 17 (28 de agosto de 2022): 2698. http://dx.doi.org/10.3390/electronics11172698.
Texto completoPanda, Rameswar, Michele Merler, Mayoore S. Jaiswal, Hui Wu, Kandan Ramakrishnan, Ulrich Finkler, Chun-Fu Richard Chen et al. "NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 10 (18 de mayo de 2021): 9294–302. http://dx.doi.org/10.1609/aaai.v35i10.17121.
Texto completoMaack, Lennart, Lennart Holstein y Alexander Schlaefer. "GANs for generation of synthetic ultrasound images from small datasets". Current Directions in Biomedical Engineering 8, n.º 1 (1 de julio de 2022): 17–20. http://dx.doi.org/10.1515/cdbme-2022-0005.
Texto completoAhmed, Shouket Abdulrahman, Hazry Desa y Abadal-Salam T. Hussain. "Aerial image semantic segmentation based on 3D fits a small dataset of 1D". IAES International Journal of Artificial Intelligence (IJ-AI) 12, n.º 4 (1 de diciembre de 2023): 2048. http://dx.doi.org/10.11591/ijai.v12.i4.pp2048-2054.
Texto completoNg, Wartini, Budiman Minasny, Brendan Malone y Patrick Filippi. "In search of an optimum sampling algorithm for prediction of soil properties from infrared spectra". PeerJ 6 (3 de octubre de 2018): e5722. http://dx.doi.org/10.7717/peerj.5722.
Texto completoZhang, Ruofan, Yi Wang, Ping Jiang, Jialiang Peng y Hailin Chen. "IBSA_Net: A Network for Tomato Leaf Disease Identification Based on Transfer Learning with Small Samples". Applied Sciences 13, n.º 7 (29 de marzo de 2023): 4348. http://dx.doi.org/10.3390/app13074348.
Texto completoMu, Lingli, Lina Xian, Lihong Li, Gang Liu, Mi Chen y Wei Zhang. "YOLO-Crater Model for Small Crater Detection". Remote Sensing 15, n.º 20 (20 de octubre de 2023): 5040. http://dx.doi.org/10.3390/rs15205040.
Texto completoShao, Ran, Xiao-Jun Bi y Zheng Chen. "A novel hybrid transformer-CNN architecture for environmental microorganism classification". PLOS ONE 17, n.º 11 (11 de noviembre de 2022): e0277557. http://dx.doi.org/10.1371/journal.pone.0277557.
Texto completoNguyen, Nhat-Duy, Tien Do, Thanh Duc Ngo y Duy-Dinh Le. "An Evaluation of Deep Learning Methods for Small Object Detection". Journal of Electrical and Computer Engineering 2020 (27 de abril de 2020): 1–18. http://dx.doi.org/10.1155/2020/3189691.
Texto completoLiu, Tengjun, Ying Chen y Wanxuan Gu. "Copyright-Certified Distillation Dataset: Distilling One Million Coins into One Bitcoin with Your Private Key". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 5 (26 de junio de 2023): 6458–66. http://dx.doi.org/10.1609/aaai.v37i5.25794.
Texto completoFinn, Michael P., Daniel R. Steinwand, Jason R. Trent, Robert A. Buehler, David M. Mattli y Kristina Haruka Yamamoto. "A Program for Handling Map Projections of Small Scale Geospatial Raster Data". Cartographic Perspectives, n.º 71 (24 de septiembre de 2012): 53–67. http://dx.doi.org/10.14714/cp71.61.
Texto completoMacKinnon, James G. "Inference with Large Clustered Datasets". Articles 92, n.º 4 (12 de julio de 2017): 649–65. http://dx.doi.org/10.7202/1040501ar.
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