Artigos de revistas sobre o tema "Dataset shift"
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Sharet, Nir, e Ilan Shimshoni. "Analyzing Data Changes using Mean Shift Clustering". International Journal of Pattern Recognition and Artificial Intelligence 30, n.º 07 (25 de maio de 2016): 1650016. http://dx.doi.org/10.1142/s0218001416500166.
Texto completo da fonteAdams, Niall. "Dataset Shift in Machine Learning". Journal of the Royal Statistical Society: Series A (Statistics in Society) 173, n.º 1 (janeiro de 2010): 274. http://dx.doi.org/10.1111/j.1467-985x.2009.00624_10.x.
Texto completo da fonteGuo, Lin Lawrence, Stephen R. Pfohl, Jason Fries, Jose Posada, Scott Lanyon Fleming, Catherine Aftandilian, Nigam Shah e Lillian Sung. "Systematic Review of Approaches to Preserve Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine". Applied Clinical Informatics 12, n.º 04 (agosto de 2021): 808–15. http://dx.doi.org/10.1055/s-0041-1735184.
Texto completo da fonteHe, Zhiqiang. "ECG Heartbeat Classification Under Dataset Shift". Journal of Intelligent Medicine and Healthcare 1, n.º 2 (2022): 79–89. http://dx.doi.org/10.32604/jimh.2022.036624.
Texto completo da fonteKim, Doyoung, Inwoong Lee, Dohyung Kim e Sanghoon Lee. "Action Recognition Using Close-Up of Maximum Activation and ETRI-Activity3D LivingLab Dataset". Sensors 21, n.º 20 (12 de outubro de 2021): 6774. http://dx.doi.org/10.3390/s21206774.
Texto completo da fonteMcGaughey, Georgia, W. Patrick Walters e Brian Goldman. "Understanding covariate shift in model performance". F1000Research 5 (7 de abril de 2016): 597. http://dx.doi.org/10.12688/f1000research.8317.1.
Texto completo da fonteMcGaughey, Georgia, W. Patrick Walters e Brian Goldman. "Understanding covariate shift in model performance". F1000Research 5 (17 de junho de 2016): 597. http://dx.doi.org/10.12688/f1000research.8317.2.
Texto completo da fonteMcGaughey, Georgia, W. Patrick Walters e Brian Goldman. "Understanding covariate shift in model performance". F1000Research 5 (17 de outubro de 2016): 597. http://dx.doi.org/10.12688/f1000research.8317.3.
Texto completo da fonteBecker, Aneta, e Jarosław Becker. "Dataset shift assessment measures in monitoring predictive models". Procedia Computer Science 192 (2021): 3391–402. http://dx.doi.org/10.1016/j.procs.2021.09.112.
Texto completo da fonteFinlayson, Samuel G., Adarsh Subbaswamy, Karandeep Singh, John Bowers, Annabel Kupke, Jonathan Zittrain, Isaac S. Kohane e Suchi Saria. "The Clinician and Dataset Shift in Artificial Intelligence". New England Journal of Medicine 385, n.º 3 (15 de julho de 2021): 283–86. http://dx.doi.org/10.1056/nejmc2104626.
Texto completo da fonteMoreno-Torres, Jose G., Troy Raeder, Rocío Alaiz-Rodríguez, Nitesh V. Chawla e Francisco Herrera. "A unifying view on dataset shift in classification". Pattern Recognition 45, n.º 1 (janeiro de 2012): 521–30. http://dx.doi.org/10.1016/j.patcog.2011.06.019.
Texto completo da fonteSubbaswamy, Adarsh, Bryant Chen e Suchi Saria. "A unifying causal framework for analyzing dataset shift-stable learning algorithms". Journal of Causal Inference 10, n.º 1 (1 de janeiro de 2022): 64–89. http://dx.doi.org/10.1515/jci-2021-0042.
Texto completo da fonteXie, Y., K. Schindler, J. Tian e X. X. Zhu. "EXPLORING CROSS-CITY SEMANTIC SEGMENTATION OF ALS POINT CLOUDS". International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2021 (28 de junho de 2021): 247–54. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2021-247-2021.
Texto completo da fonteZHAO, YUZHONG, BABAK ALIPANAHI, SHUAI CHENG LI e MING LI. "PROTEIN SECONDARY STRUCTURE PREDICTION USING NMR CHEMICAL SHIFT DATA". Journal of Bioinformatics and Computational Biology 08, n.º 05 (outubro de 2010): 867–84. http://dx.doi.org/10.1142/s0219720010004987.
Texto completo da fonteChakraborty, Saptarshi, Debolina Paul e Swagatam Das. "Automated Clustering of High-dimensional Data with a Feature Weighted Mean Shift Algorithm". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 8 (18 de maio de 2021): 6930–38. http://dx.doi.org/10.1609/aaai.v35i8.16854.
Texto completo da fonteTasche, Dirk. "Factorizable Joint Shift in Multinomial Classification". Machine Learning and Knowledge Extraction 4, n.º 3 (10 de setembro de 2022): 779–802. http://dx.doi.org/10.3390/make4030038.
Texto completo da fonteXue, Zhiyun, Feng Yang, Sivaramakrishnan Rajaraman, Ghada Zamzmi e Sameer Antani. "Cross Dataset Analysis of Domain Shift in CXR Lung Region Detection". Diagnostics 13, n.º 6 (11 de março de 2023): 1068. http://dx.doi.org/10.3390/diagnostics13061068.
Texto completo da fonteSáez, José A., e José L. Romero-Béjar. "Impact of Regressand Stratification in Dataset Shift Caused by Cross-Validation". Mathematics 10, n.º 14 (21 de julho de 2022): 2538. http://dx.doi.org/10.3390/math10142538.
Texto completo da fonteTurhan, Burak. "On the dataset shift problem in software engineering prediction models". Empirical Software Engineering 17, n.º 1-2 (12 de outubro de 2011): 62–74. http://dx.doi.org/10.1007/s10664-011-9182-8.
Texto completo da fonteBecker, Jarosław, e Aneta Becker. "Predictive Accuracy Index in evaluating the dataset shift (case study)". Procedia Computer Science 225 (2023): 3342–51. http://dx.doi.org/10.1016/j.procs.2023.10.328.
Texto completo da fonteAryal, Jagannath, e Bipul Neupane. "Multi-Scale Feature Map Aggregation and Supervised Domain Adaptation of Fully Convolutional Networks for Urban Building Footprint Extraction". Remote Sensing 15, n.º 2 (13 de janeiro de 2023): 488. http://dx.doi.org/10.3390/rs15020488.
Texto completo da fontePeng, Zhiyong, Changlin Han, Yadong Liu e Zongtan Zhou. "Weighted Policy Constraints for Offline Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 8 (26 de junho de 2023): 9435–43. http://dx.doi.org/10.1609/aaai.v37i8.26130.
Texto completo da fontePhongsasiri, Siriwan, e Suwanna Rasmequan. "Outlier Detection in Wellness Data using Probabilistic Mapped Mean-Shift Algorithms". ECTI Transactions on Computer and Information Technology (ECTI-CIT) 15, n.º 2 (11 de agosto de 2021): 258–66. http://dx.doi.org/10.37936/ecti-cit.2021152.244971.
Texto completo da fonteRodriguez-Vazquez, Javier, Miguel Fernandez-Cortizas, David Perez-Saura, Martin Molina e Pascual Campoy. "Overcoming Domain Shift in Neural Networks for Accurate Plant Counting in Aerial Images". Remote Sensing 15, n.º 6 (22 de março de 2023): 1700. http://dx.doi.org/10.3390/rs15061700.
Texto completo da fonteTappy, Nicolas, Anna Fontcuberta i Morral e Christian Monachon. "Image shift correction, noise analysis, and model fitting of (cathodo-)luminescence hyperspectral maps". Review of Scientific Instruments 93, n.º 5 (1 de maio de 2022): 053702. http://dx.doi.org/10.1063/5.0080486.
Texto completo da fonteWang, Li, Dong Li, Han Liu, JinZhang Peng, Lu Tian e Yi Shan. "Cross-Dataset Collaborative Learning for Semantic Segmentation in Autonomous Driving". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 3 (28 de junho de 2022): 2487–94. http://dx.doi.org/10.1609/aaai.v36i3.20149.
Texto completo da fonteHe, Yue, Xinwei Shen, Renzhe Xu, Tong Zhang, Yong Jiang, Wenchao Zou e Peng Cui. "Covariate-Shift Generalization via Random Sample Weighting". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 10 (26 de junho de 2023): 11828–36. http://dx.doi.org/10.1609/aaai.v37i10.26396.
Texto completo da fonteHong, Zhiqing, Zelong Li, Shuxin Zhong, Wenjun Lyu, Haotian Wang, Yi Ding, Tian He e Desheng Zhang. "CrossHAR: Generalizing Cross-dataset Human Activity Recognition via Hierarchical Self-Supervised Pretraining". Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 8, n.º 2 (13 de maio de 2024): 1–26. http://dx.doi.org/10.1145/3659597.
Texto completo da fonteWei, Weiwei, Yuxuan Liao, Yufei Wang, Shaoqi Wang, Wen Du, Hongmei Lu, Bo Kong, Huawu Yang e Zhimin Zhang. "Deep Learning-Based Method for Compound Identification in NMR Spectra of Mixtures". Molecules 27, n.º 12 (7 de junho de 2022): 3653. http://dx.doi.org/10.3390/molecules27123653.
Texto completo da fonteBlanza, J., X. E. Cabasal, J. B. Cipriano, G. A. Guerrero, R. Y. Pescador e E. V. Rivera. "Indoor Wireless Multipaths Outlier Detection and Clustering". Journal of Physics: Conference Series 2356, n.º 1 (1 de outubro de 2022): 012037. http://dx.doi.org/10.1088/1742-6596/2356/1/012037.
Texto completo da fonteGoel, Parth, e Amit Ganatra. "Unsupervised Domain Adaptation for Image Classification and Object Detection Using Guided Transfer Learning Approach and JS Divergence". Sensors 23, n.º 9 (30 de abril de 2023): 4436. http://dx.doi.org/10.3390/s23094436.
Texto completo da fonteKushol, Rafsanjany, Alan H. Wilman, Sanjay Kalra e Yee-Hong Yang. "DSMRI: Domain Shift Analyzer for Multi-Center MRI Datasets". Diagnostics 13, n.º 18 (14 de setembro de 2023): 2947. http://dx.doi.org/10.3390/diagnostics13182947.
Texto completo da fonteSinha, Samarth, Homanga Bharadhwaj, Anirudh Goyal, Hugo Larochelle, Animesh Garg e Florian Shkurti. "DIBS: Diversity Inducing Information Bottleneck in Model Ensembles". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 11 (18 de maio de 2021): 9666–74. http://dx.doi.org/10.1609/aaai.v35i11.17163.
Texto completo da fonteHeffington, Colton, Brandon Beomseob Park e Laron K. Williams. "The “Most Important Problem” Dataset (MIPD): a new dataset on American issue importance". Conflict Management and Peace Science 36, n.º 3 (31 de março de 2017): 312–35. http://dx.doi.org/10.1177/0738894217691463.
Texto completo da fonteGuo, Fumin, Matthew Ng, Maged Goubran, Steffen E. Petersen, Stefan K. Piechnik, Stefan Neubauer e Graham Wright. "Improving cardiac MRI convolutional neural network segmentation on small training datasets and dataset shift: A continuous kernel cut approach". Medical Image Analysis 61 (abril de 2020): 101636. http://dx.doi.org/10.1016/j.media.2020.101636.
Texto completo da fonteVescovi, R. F. C., M. B. Cardoso e E. X. Miqueles. "Radiography registration for mosaic tomography". Journal of Synchrotron Radiation 24, n.º 3 (7 de abril de 2017): 686–94. http://dx.doi.org/10.1107/s1600577517001953.
Texto completo da fonteTraynor, Carlos, Tarjinder Sahota, Helen Tomkinson, Ignacio Gonzalez-Garcia, Neil Evans e Michael Chappell. "Imputing Biomarker Status from RWE Datasets—A Comparative Study". Journal of Personalized Medicine 11, n.º 12 (13 de dezembro de 2021): 1356. http://dx.doi.org/10.3390/jpm11121356.
Texto completo da fonteWang, Xiaoyang, Chen Li, Jianqiao Zhao e Dong Yu. "NaturalConv: A Chinese Dialogue Dataset Towards Multi-turn Topic-driven Conversation". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 16 (18 de maio de 2021): 14006–14. http://dx.doi.org/10.1609/aaai.v35i16.17649.
Texto completo da fonteHuch, Sebastian, e Markus Lienkamp. "Towards Minimizing the LiDAR Sim-to-Real Domain Shift: Object-Level Local Domain Adaptation for 3D Point Clouds of Autonomous Vehicles". Sensors 23, n.º 24 (18 de dezembro de 2023): 9913. http://dx.doi.org/10.3390/s23249913.
Texto completo da fonteOthman, Walaa, Alexey Kashevnik, Ammar Ali e Nikolay Shilov. "DriverMVT: In-Cabin Dataset for Driver Monitoring including Video and Vehicle Telemetry Information". Data 7, n.º 5 (11 de maio de 2022): 62. http://dx.doi.org/10.3390/data7050062.
Texto completo da fonteIshihara, Kazuaki, e Koutarou Matsumoto. "Comparing the Robustness of ResNet, Swin-Transformer, and MLP-Mixer under Unique Distribution Shifts in Fundus Images". Bioengineering 10, n.º 12 (1 de dezembro de 2023): 1383. http://dx.doi.org/10.3390/bioengineering10121383.
Texto completo da fonteTakahashi, Satoshi, Masamichi Takahashi, Manabu Kinoshita, Mototaka Miyake, Jun Sese, Kazuma Kobayashi, Koichi Ichimura, Yoshitaka Narita, Ryuji Hamamoto e Consortium of Molecular Diagnosis of glioma. "RBIO-03. INITIAL RESULT OF DEVELOP ROBUST DEEP LEARNING MODEL FOR DETECTING GENOMIC STATUS IN GLIOMAS AGAINST IMAGE DIFFERENCES AMONG FACILITIES". Neuro-Oncology 23, Supplement_6 (2 de novembro de 2021): vi192. http://dx.doi.org/10.1093/neuonc/noab196.760.
Texto completo da fonteAllen, Robert C., Mattia C. Bertazzini e Leander Heldring. "The Economic Origins of Government". American Economic Review 113, n.º 10 (1 de outubro de 2023): 2507–45. http://dx.doi.org/10.1257/aer.20201919.
Texto completo da fonteWu, Teng, Bruno Vallet, Marc Pierrot-Deseilligny e Ewelina Rupnik. "An evaluation of Deep Learning based stereo dense matching dataset shift from aerial images and a large scale stereo dataset". International Journal of Applied Earth Observation and Geoinformation 128 (abril de 2024): 103715. http://dx.doi.org/10.1016/j.jag.2024.103715.
Texto completo da fonteAsopa, U., S. Kumar e P. K. Thakur. "PSInSAR Study of Lyngenfjord Norway, using TerraSAR-X Data". ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-5 (15 de novembro de 2018): 245–51. http://dx.doi.org/10.5194/isprs-annals-iv-5-245-2018.
Texto completo da fonteTang, Yansong, Xingyu Liu, Xumin Yu, Danyang Zhang, Jiwen Lu e Jie Zhou. "Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition". ACM Transactions on Multimedia Computing, Communications, and Applications 18, n.º 2 (31 de maio de 2022): 1–24. http://dx.doi.org/10.1145/3472722.
Texto completo da fonteGuentchev, Galina, Joseph J. Barsugli e Jon Eischeid. "Homogeneity of Gridded Precipitation Datasets for the Colorado River Basin". Journal of Applied Meteorology and Climatology 49, n.º 12 (1 de dezembro de 2010): 2404–15. http://dx.doi.org/10.1175/2010jamc2484.1.
Texto completo da fonteSime, Louise C., Richard C. A. Hindmarsh e Hugh Corr. "Automated processing to derive dip angles of englacial radar reflectors in ice sheets". Journal of Glaciology 57, n.º 202 (2011): 260–66. http://dx.doi.org/10.3189/002214311796405870.
Texto completo da fonteSharif, Muhammad Imran, Muhammad Attique Khan, Abdullah Alqahtani, Muhammad Nazir, Shtwai Alsubai, Adel Binbusayyis e Robertas Damaševičius. "Deep Learning and Kurtosis-Controlled, Entropy-Based Framework for Human Gait Recognition Using Video Sequences". Electronics 11, n.º 3 (21 de janeiro de 2022): 334. http://dx.doi.org/10.3390/electronics11030334.
Texto completo da fonteHidalgo Davila, Mateo, Maria Baldeon-Calisto, Juan Jose Murillo, Bernardo Puente-Mejia, Danny Navarrete, Daniel Riofrío, Noel Peréz, Diego S. Benítez e Ricardo Flores Moyano. "Analyzing the Effect of Basic Data Augmentation for COVID-19 Detection through a Fractional Factorial Experimental Design". Emerging Science Journal 7 (24 de setembro de 2022): 1–16. http://dx.doi.org/10.28991/esj-2023-sper-01.
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