Literatura científica selecionada sobre o tema "Dataset shift"
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Artigos de revistas sobre o assunto "Dataset shift"
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 fonteTeses / dissertações sobre o assunto "Dataset shift"
Wang, Fulton. "Addressing two issues in machine learning : interpretability and dataset shift". Thesis, Massachusetts Institute of Technology, 2018. https://hdl.handle.net/1721.1/122870.
Texto completo da fonteCataloged from PDF version of thesis.
Includes bibliographical references (pages 71-77).
In this thesis, I create solutions to two problems. In the first, I address the problem that many machine learning models are not interpretable, by creating a new form of classifier, called the Falling Rule List. This is a decision list classifier where the predicted probabilities are decreasing down the list. Experiments show that the gain in interpretability need not be accompanied by a large sacrifice in accuracy on real world datasets. I then briefly discuss possible extensions that allow one to directly optimize rank statistics over rule lists, and handle ordinal data. In the second, I address a shortcoming of a popular approach to handling covariate shift, in which the training distribution and that for which predictions need to be made have different covariate distributions. In particular, the existing importance weighting approach to handling covariate shift suffers from high variance if the two covariate distributions are very different. I develop a dimension reduction procedure that reduces this variance, at the expense of increased bias. Experiments show that this tradeoff can be worthwhile in some situations.
by Fulton Wang.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Gogolashvili, Davit. "Global and local Kernel methods for dataset shift, scalable inference and optimization". Electronic Thesis or Diss., Sorbonne université, 2022. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2022SORUS363v2.pdf.
Texto completo da fonteIn many real world problems, the training data and test data have different distributions. The most common settings for dataset shift often considered in the literature are covariate shift and target shift. In this thesis, we investigate nonparametric models applied to the dataset shift scenario. We develop a novel framework to accelerate Gaussian process regression. In particular, we consider localization kernels at each data point to down-weigh the contributions from other data points that are far away, and we derive the GPR model stemming from the application of such localization operation. We propose a new method for estimating the minimizer and the minimum value of a smooth and strongly convex regression function from the observations contaminated by random noise
Spooner, Amy. "Developing a minimum dataset for nursing team leader handover in the intensive care unit: a prospective interventional study". Thesis, Griffith University, 2018. http://hdl.handle.net/10072/382227.
Texto completo da fonteThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Nursing & Midwifery
Griffith Health
Full Text
Fonseca, Eduardo. "Training sound event classifiers using different types of supervision". Doctoral thesis, Universitat Pompeu Fabra, 2021. http://hdl.handle.net/10803/673067.
Texto completo da fonteEl interés en el reconocimiento automático de eventos sonoros se ha incrementado en los últimos años, motivado por nuevas aplicaciones en campos como la asistencia médica, smart homes, o urbanismo. Al comienzo de esta tesis, la investigación en clasificación de eventos sonoros se centraba principalmente en aprendizaje supervisado usando datasets pequeños, a menudo anotados cuidadosamente con vocabularios limitados a dominios específicos (como el urbano o el doméstico). Sin embargo, tales datasets no permiten entrenar clasificadores capaces de reconocer los cientos de eventos sonoros que ocurren en nuestro entorno, como silbidos de kettle, sonidos de pájaros, coches pasando, o diferentes alarmas. Al mismo tiempo, websites como Freesound o YouTube albergan grandes cantidades de datos de sonido ambiental, que pueden ser útiles para entrenar clasificadores con un vocabulario más extenso, particularmente utilizando métodos de deep learning que requieren gran cantidad de datos. Para avanzar el estado del arte en la clasificación de eventos sonoros, esta tesis investiga varios aspectos de la creación de datasets, así como de aprendizaje supervisado y no supervisado para entrenar clasificadores de eventos sonoros con un vocabulario extenso, utilizando diferentes tipos de supervisión de manera novedosa y alternativa. En concreto, nos centramos en aprendizaje supervisado usando etiquetas sin ruido y con ruido, así como en aprendizaje de representaciones auto-supervisado a partir de datos no etiquetados. La primera parte de esta tesis se centra en la creación de FSD50K, un dataset con más de 100h de audio etiquetado manualmente usando 200 clases de eventos sonoros. Presentamos una descripción detallada del proceso de creación y una caracterización exhaustiva del dataset. Además, exploramos modificaciones arquitectónicas para aumentar la invariancia frente a desplazamientos en CNNs, mejorando la robustez frente a desplazamientos de tiempo/frecuencia en los espectrogramas de entrada. En la segunda parte, nos centramos en entrenar clasificadores de eventos sonoros usando etiquetas con ruido. Primero, proponemos un dataset que permite la investigación del ruido de etiquetas real. Después, exploramos métodos agnósticos a la arquitectura de red para mitigar el efecto del ruido en las etiquetas durante el entrenamiento, incluyendo técnicas de regularización, funciones de coste robustas al ruido, y estrategias para rechazar ejemplos etiquetados con ruido. Además, desarrollamos un método teacher-student para abordar el problema de las etiquetas ausentes en datasets de eventos sonoros. En la tercera parte, proponemos algoritmos para aprender representaciones de audio a partir de datos sin etiquetar. En particular, desarrollamos métodos de aprendizaje contrastivos auto-supervisados, donde las representaciones se aprenden comparando pares de ejemplos calculados a través de métodos de aumento de datos y separación automática de sonido. Finalmente, reportamos sobre la organización de dos DCASE Challenge Tasks para el tageado automático de audio a partir de etiquetas ruidosas. Mediante la propuesta de datasets, así como de métodos de vanguardia y representaciones de audio, esta tesis contribuye al avance de la investigación abierta sobre eventos sonoros y a la transición del aprendizaje supervisado tradicional utilizando etiquetas sin ruido a otras estrategias de aprendizaje menos dependientes de costosos esfuerzos de anotación.
Sarr, Jean Michel Amath. "Étude de l’augmentation de données pour la robustesse des réseaux de neurones profonds". Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS072.
Texto completo da fonteIn this thesis, we considered the problem of the robustness of neural networks. That is, we have considered the case where the learning set and the deployment set are not independently and identically distributed from the same source. This hypothesis is called : the i.i.d hypothesis. Our main research axis has been data augmentation. Indeed, an extensive literature review and preliminary experiments showed us the regularization potential of data augmentation. Thus, as a first step, we sought to use data augmentation to make neural networks more robust to various synthetic and natural dataset shifts. A dataset shift being simply a violation of the i.i.d assumption. However, the results of this approach have been mixed. Indeed, we observed that in some cases the augmented data could lead to performance jumps on the deployment set. But this phenomenon did not occur every time. In some cases, the augmented data could even reduce performance on the deployment set. In our conclusion, we offer a granular explanation for this phenomenon. Better use of data augmentation toward neural network robustness is to generate stress tests to observe a model behavior when various shift occurs. Then, to use that information to estimate the error on the deployment set of interest even without labels, we call this deployment error estimation. Furthermore, we show that the use of independent data augmentation can improve deployment error estimation. We believe that this use of data augmentation will allow us to better quantify the reliability of neural networks when deployed on new unknown datasets
Vanck, Thomas [Verfasser], Jochen [Akademischer Betreuer] Garcke, Jochen [Gutachter] Garcke e Reinhold [Gutachter] Schneider. "New importance sampling based algorithms for compensating dataset shifts / Thomas Vanck ; Gutachter: Jochen Garcke, Reinhold Schneider ; Betreuer: Jochen Garcke". Berlin : Technische Universität Berlin, 2016. http://d-nb.info/1156012562/34.
Texto completo da fonteLuus, Francois Pierre Sarel. "Dataset shift in land-use classification for optical remote sensing". Thesis, 2016. http://hdl.handle.net/2263/56246.
Texto completo da fonteThesis (PhD)--University of Pretoria, 2016.
National Research Foundation (NRF)
University of Pretoria (UP)
Electrical, Electronic and Computer Engineering
PhD
Unrestricted
Livros sobre o assunto "Dataset shift"
Quiñonero-Candela, Joaquin. Dataset shift in machine learning. Cambridge, MA: MIT Press, 2009.
Encontre o texto completo da fonteQuiñonero-Candela, Joaquin, Masashi Sugiyama, Anton Schwaighofer e Neil D. Lawrence, eds. Dataset Shift in Machine Learning. The MIT Press, 2008. http://dx.doi.org/10.7551/mitpress/9780262170055.001.0001.
Texto completo da fonteSchwaighofer, Anton, Joaquin Quiñonero-Candela, Masashi Sugiyama e Neil D. Lawrence. Dataset Shift in Machine Learning. MIT Press, 2018.
Encontre o texto completo da fonteSchwaighofer, Anton, Masashi Sugiyama, Neil D. Lawrence e Joaquin Quinonero-Candela. Dataset Shift in Machine Learning. MIT Press, 2022.
Encontre o texto completo da fonteOgorzalek, Thomas K. The Cities on the Hill. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190668877.003.0006.
Texto completo da fonteLoyle, Cyanne E. Transitional Justice During Armed Conflict. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228637.013.218.
Texto completo da fontePoplack, Shana. Borrowing. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190256388.001.0001.
Texto completo da fonteCapítulos de livros sobre o assunto "Dataset shift"
da Silva, Camilla, Jed Nisenson e Jeff Boisvert. "Comparing and Detecting Stationarity and Dataset Shift". In Springer Proceedings in Earth and Environmental Sciences, 37–42. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-19845-8_3.
Texto completo da fonteQian, Hongyi, Baohui Wang, Ping Ma, Lei Peng, Songfeng Gao e You Song. "Managing Dataset Shift by Adversarial Validation for Credit Scoring". In Lecture Notes in Computer Science, 477–88. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20862-1_35.
Texto completo da fonteEsuli, Andrea, Alessandro Fabris, Alejandro Moreo e Fabrizio Sebastiani. "The Case for Quantification". In The Information Retrieval Series, 1–17. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-20467-8_1.
Texto completo da fonteLeyendecker, Lars, Shobhit Agarwal, Thorben Werner, Maximilian Motz e Robert H. Schmitt. "A Study on Data Augmentation Techniques for Visual Defect Detection in Manufacturing". In Bildverarbeitung in der Automation, 73–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-66769-9_6.
Texto completo da fonteXia, Tong, Jing Han e Cecilia Mascolo. "Benchmarking Uncertainty Quantification on Biosignal Classification Tasks Under Dataset Shift". In Multimodal AI in Healthcare, 347–59. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14771-5_25.
Texto completo da fonteRaza, Haider, Girijesh Prasad e Yuhua Li. "EWMA Based Two-Stage Dataset Shift-Detection in Non-stationary Environments". In IFIP Advances in Information and Communication Technology, 625–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41142-7_63.
Texto completo da fonteJin, Qiao, Haoyang Ding, Linfeng Li, Haitao Huang, Lei Wang e Jun Yan. "Tackling MeSH Indexing Dataset Shift with Time-Aware Concept Embedding Learning". In Database Systems for Advanced Applications, 474–88. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59419-0_29.
Texto completo da fonteZhu, Calvin, Michael D. Noseworthy e Thomas E. Doyle. "Addressing Dataset Shift for Trustworthy Deep Learning Diagnostic Ultrasound Decision Support". In Lecture Notes in Computer Science, 110–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-67868-8_7.
Texto completo da fonteZhang, Jiaxin, Tomohiro Fukuda e Nobuyoshi Yabuki. "A Large-Scale Measurement and Quantitative Analysis Method of Façade Color in the Urban Street Using Deep Learning". In Proceedings of the 2020 DigitalFUTURES, 93–102. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4400-6_9.
Texto completo da fonteRizvi, Syed Zeeshan, Muhammad Umar Farooq e Rana Hammad Raza. "Performance Comparison of Deep Residual Networks-Based Super Resolution Algorithms Using Thermal Images: Case Study of Crowd Counting". In Digital Interaction and Machine Intelligence, 75–87. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11432-8_7.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Dataset shift"
Maggio, Simona, Victor Bouvier e Leo Dreyfus-Schmidt. "Performance Prediction Under Dataset Shift". In 2022 26th International Conference on Pattern Recognition (ICPR). IEEE, 2022. http://dx.doi.org/10.1109/icpr56361.2022.9956676.
Texto completo da fonteTuia, Devis, Edoardo Pasolli e William J. Emery. "Dataset shift adaptation with active queries". In 2011 Joint Urban Remote Sensing Event (JURSE). IEEE, 2011. http://dx.doi.org/10.1109/jurse.2011.5764734.
Texto completo da fonteSpence, David, Christopher Inskip, Novi Quadrianto e David Weir. "Quantification under class-conditional dataset shift". In ASONAM '19: International Conference on Advances in Social Networks Analysis and Mining. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3341161.3342948.
Texto completo da fonteTakahashi, Carla C., Luiz C. B. Torres e Antonio P. Braga. "Gabriel Graph Transductive Approach to Dataset Shift". In 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2019. http://dx.doi.org/10.1109/codit.2019.8820327.
Texto completo da fonteBrugman, Simon, Tomas Sostak, Pradyot Patil e Max Baak. "popmon: Analysis Package for Dataset Shift Detection". In Python in Science Conference. SciPy, 2022. http://dx.doi.org/10.25080/majora-212e5952-01d.
Texto completo da fonteLucas, Yvan, Pierre-Edouard Portier, Lea Laporte, Sylvie Calabretto, Liyun He-Guelton, Frederic Oble e Michael Granitzer. "Dataset Shift Quantification for Credit Card Fraud Detection". In 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE). IEEE, 2019. http://dx.doi.org/10.1109/aike.2019.00024.
Texto completo da fonteWang, Ziming, Changwu Huang e Xin Yao. "Feature Attribution Explanation to Detect Harmful Dataset Shift". In 2023 International Joint Conference on Neural Networks (IJCNN). IEEE, 2023. http://dx.doi.org/10.1109/ijcnn54540.2023.10191221.
Texto completo da fonteChen, Bo, Wai Lam, Ivor Tsang e Tak-Lam Wong. "Location and Scatter Matching for Dataset Shift in Text Mining". In 2010 IEEE 10th International Conference on Data Mining (ICDM). IEEE, 2010. http://dx.doi.org/10.1109/icdm.2010.72.
Texto completo da fonteRaza, Haider, Girijesh Prasad e Yuhua Li. "Dataset Shift Detection in Non-stationary Environments Using EWMA Charts". In 2013 IEEE International Conference on Systems, Man and Cybernetics (SMC 2013). IEEE, 2013. http://dx.doi.org/10.1109/smc.2013.537.
Texto completo da fonteDenham, Benjamin, Edmund M.-K. Lai, Roopak Sinha e M. Asif Naeem. "Gain-Some-Lose-Some: Reliable Quantification Under General Dataset Shift". In 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 2021. http://dx.doi.org/10.1109/icdm51629.2021.00121.
Texto completo da fonteRelatórios de organizações sobre o assunto "Dataset shift"
Mascagni, Giulia, e Fabrizio Santoro. The Tax Side of the Pandemic: Compliance Shifts and Funding for Recovery in Rwanda. Institute of Development Studies, outubro de 2021. http://dx.doi.org/10.19088/ictd.2021.019.
Texto completo da fonteClark, Andrew E., Angela Greulich e Hippolyte d’Albis. The age U-shape in Europe: the protective role of partnership. Verlag der Österreichischen Akademie der Wissenschaften, março de 2021. http://dx.doi.org/10.1553/populationyearbook2021.res3.1.
Texto completo da fonteTait, Emma, Pia Ruisi-Besares, Matthias Sirch, Alyx Belisle, Jennifer Pontius e Elissa Schuett. Technical Report: Monitoring and Communicating Changes in Disturbance Regimes (Version 1.0). Forest Ecosystem Monitoring Cooperative, outubro de 2021. http://dx.doi.org/10.18125/cc0a0l.
Texto completo da fonteCalcagno, Juan Carlos, e Mariana Alfonso. Minority Enrollments at Public Universities of Diverse Selectivity Levels under Different Admission Regimes: The Case of Texas. Inter-American Development Bank, outubro de 2007. http://dx.doi.org/10.18235/0010878.
Texto completo da fonteLinker, Taylor, e Timothy Jacobs. PR-457-18204-R02 Variable Fuel Effects on Legacy Compressor Engines Phase V Engine Control Enhancement. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), julho de 2020. http://dx.doi.org/10.55274/r0011729.
Texto completo da fonteBaxter, W., Amanda Barker, Samuel Beal, Lauren Bosche, Ryan Busby, Zoe Courville, Elias Deeb et al. A comprehensive approach to data collection, management, and visualization for terrain characterization in cold regions. Engineer Research and Development Center (U.S.), fevereiro de 2024. http://dx.doi.org/10.21079/11681/48212.
Texto completo da fonteLu, Tianjun, Jian-yu Ke, Fynnwin Prager e Jose N. Martinez. “TELE-commuting” During the COVID-19 Pandemic and Beyond: Unveiling State-wide Patterns and Trends of Telecommuting in Relation to Transportation, Employment, Land Use, and Emissions in Calif. Mineta Transportation Institute, agosto de 2022. http://dx.doi.org/10.31979/mti.2022.2147.
Texto completo da fonteMoreda, Fekadu, Benjamin Lord, Mauro Nalesso, Pedro Coli Valdes Daussa e Juliana Corrales. Hydro-BID: New Functionalities (Reservoir, Sediment and Groundwater Simulation Modules). Inter-American Development Bank, novembro de 2016. http://dx.doi.org/10.18235/0009312.
Texto completo da fonteHeifetz, Yael, e Michael Bender. Success and failure in insect fertilization and reproduction - the role of the female accessory glands. United States Department of Agriculture, dezembro de 2006. http://dx.doi.org/10.32747/2006.7695586.bard.
Texto completo da fonteAllen, Kathy, Andy Nadeau e Andy Robertston. Natural resource condition assessment: Salinas Pueblo Missions National Monument. National Park Service, maio de 2022. http://dx.doi.org/10.36967/nrr-2293613.
Texto completo da fonte