Auswahl der wissenschaftlichen Literatur zum Thema „Dataset shift“
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Zeitschriftenartikel zum Thema "Dataset shift"
Sharet, Nir, und Ilan Shimshoni. „Analyzing Data Changes using Mean Shift Clustering“. International Journal of Pattern Recognition and Artificial Intelligence 30, Nr. 07 (25.05.2016): 1650016. http://dx.doi.org/10.1142/s0218001416500166.
Der volle Inhalt der QuelleAdams, Niall. „Dataset Shift in Machine Learning“. Journal of the Royal Statistical Society: Series A (Statistics in Society) 173, Nr. 1 (Januar 2010): 274. http://dx.doi.org/10.1111/j.1467-985x.2009.00624_10.x.
Der volle Inhalt der QuelleGuo, Lin Lawrence, Stephen R. Pfohl, Jason Fries, Jose Posada, Scott Lanyon Fleming, Catherine Aftandilian, Nigam Shah und 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, Nr. 04 (August 2021): 808–15. http://dx.doi.org/10.1055/s-0041-1735184.
Der volle Inhalt der QuelleHe, Zhiqiang. „ECG Heartbeat Classification Under Dataset Shift“. Journal of Intelligent Medicine and Healthcare 1, Nr. 2 (2022): 79–89. http://dx.doi.org/10.32604/jimh.2022.036624.
Der volle Inhalt der QuelleKim, Doyoung, Inwoong Lee, Dohyung Kim und Sanghoon Lee. „Action Recognition Using Close-Up of Maximum Activation and ETRI-Activity3D LivingLab Dataset“. Sensors 21, Nr. 20 (12.10.2021): 6774. http://dx.doi.org/10.3390/s21206774.
Der volle Inhalt der QuelleMcGaughey, Georgia, W. Patrick Walters und Brian Goldman. „Understanding covariate shift in model performance“. F1000Research 5 (07.04.2016): 597. http://dx.doi.org/10.12688/f1000research.8317.1.
Der volle Inhalt der QuelleMcGaughey, Georgia, W. Patrick Walters und Brian Goldman. „Understanding covariate shift in model performance“. F1000Research 5 (17.06.2016): 597. http://dx.doi.org/10.12688/f1000research.8317.2.
Der volle Inhalt der QuelleMcGaughey, Georgia, W. Patrick Walters und Brian Goldman. „Understanding covariate shift in model performance“. F1000Research 5 (17.10.2016): 597. http://dx.doi.org/10.12688/f1000research.8317.3.
Der volle Inhalt der QuelleBecker, Aneta, und 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.
Der volle Inhalt der QuelleFinlayson, Samuel G., Adarsh Subbaswamy, Karandeep Singh, John Bowers, Annabel Kupke, Jonathan Zittrain, Isaac S. Kohane und Suchi Saria. „The Clinician and Dataset Shift in Artificial Intelligence“. New England Journal of Medicine 385, Nr. 3 (15.07.2021): 283–86. http://dx.doi.org/10.1056/nejmc2104626.
Der volle Inhalt der QuelleDissertationen zum Thema "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.
Der volle Inhalt der QuelleCataloged 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.
Der volle Inhalt der QuelleIn 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.
Der volle Inhalt der QuelleThesis (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.
Der volle Inhalt der QuelleEl 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.
Der volle Inhalt der QuelleIn 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 und 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.
Der volle Inhalt der QuelleLuus, Francois Pierre Sarel. „Dataset shift in land-use classification for optical remote sensing“. Thesis, 2016. http://hdl.handle.net/2263/56246.
Der volle Inhalt der QuelleThesis (PhD)--University of Pretoria, 2016.
National Research Foundation (NRF)
University of Pretoria (UP)
Electrical, Electronic and Computer Engineering
PhD
Unrestricted
Bücher zum Thema "Dataset shift"
Quiñonero-Candela, Joaquin. Dataset shift in machine learning. Cambridge, MA: MIT Press, 2009.
Den vollen Inhalt der Quelle findenQuiñonero-Candela, Joaquin, Masashi Sugiyama, Anton Schwaighofer und Neil D. Lawrence, Hrsg. Dataset Shift in Machine Learning. The MIT Press, 2008. http://dx.doi.org/10.7551/mitpress/9780262170055.001.0001.
Der volle Inhalt der QuelleSchwaighofer, Anton, Joaquin Quiñonero-Candela, Masashi Sugiyama und Neil D. Lawrence. Dataset Shift in Machine Learning. MIT Press, 2018.
Den vollen Inhalt der Quelle findenSchwaighofer, Anton, Masashi Sugiyama, Neil D. Lawrence und Joaquin Quinonero-Candela. Dataset Shift in Machine Learning. MIT Press, 2022.
Den vollen Inhalt der Quelle findenOgorzalek, Thomas K. The Cities on the Hill. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190668877.003.0006.
Der volle Inhalt der QuelleLoyle, Cyanne E. Transitional Justice During Armed Conflict. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228637.013.218.
Der volle Inhalt der QuellePoplack, Shana. Borrowing. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190256388.001.0001.
Der volle Inhalt der QuelleBuchteile zum Thema "Dataset shift"
da Silva, Camilla, Jed Nisenson und 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.
Der volle Inhalt der QuelleQian, Hongyi, Baohui Wang, Ping Ma, Lei Peng, Songfeng Gao und 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.
Der volle Inhalt der QuelleEsuli, Andrea, Alessandro Fabris, Alejandro Moreo und 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.
Der volle Inhalt der QuelleLeyendecker, Lars, Shobhit Agarwal, Thorben Werner, Maximilian Motz und 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.
Der volle Inhalt der QuelleXia, Tong, Jing Han und 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.
Der volle Inhalt der QuelleRaza, Haider, Girijesh Prasad und 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.
Der volle Inhalt der QuelleJin, Qiao, Haoyang Ding, Linfeng Li, Haitao Huang, Lei Wang und 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.
Der volle Inhalt der QuelleZhu, Calvin, Michael D. Noseworthy und 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.
Der volle Inhalt der QuelleZhang, Jiaxin, Tomohiro Fukuda und 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.
Der volle Inhalt der QuelleRizvi, Syed Zeeshan, Muhammad Umar Farooq und 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.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Dataset shift"
Maggio, Simona, Victor Bouvier und 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.
Der volle Inhalt der QuelleTuia, Devis, Edoardo Pasolli und 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.
Der volle Inhalt der QuelleSpence, David, Christopher Inskip, Novi Quadrianto und 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.
Der volle Inhalt der QuelleTakahashi, Carla C., Luiz C. B. Torres und 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.
Der volle Inhalt der QuelleBrugman, Simon, Tomas Sostak, Pradyot Patil und 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.
Der volle Inhalt der QuelleLucas, Yvan, Pierre-Edouard Portier, Lea Laporte, Sylvie Calabretto, Liyun He-Guelton, Frederic Oble und 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.
Der volle Inhalt der QuelleWang, Ziming, Changwu Huang und 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.
Der volle Inhalt der QuelleChen, Bo, Wai Lam, Ivor Tsang und 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.
Der volle Inhalt der QuelleRaza, Haider, Girijesh Prasad und 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.
Der volle Inhalt der QuelleDenham, Benjamin, Edmund M.-K. Lai, Roopak Sinha und 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.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Dataset shift"
Mascagni, Giulia, und Fabrizio Santoro. The Tax Side of the Pandemic: Compliance Shifts and Funding for Recovery in Rwanda. Institute of Development Studies, Oktober 2021. http://dx.doi.org/10.19088/ictd.2021.019.
Der volle Inhalt der QuelleClark, Andrew E., Angela Greulich und Hippolyte d’Albis. The age U-shape in Europe: the protective role of partnership. Verlag der Österreichischen Akademie der Wissenschaften, März 2021. http://dx.doi.org/10.1553/populationyearbook2021.res3.1.
Der volle Inhalt der QuelleTait, Emma, Pia Ruisi-Besares, Matthias Sirch, Alyx Belisle, Jennifer Pontius und Elissa Schuett. Technical Report: Monitoring and Communicating Changes in Disturbance Regimes (Version 1.0). Forest Ecosystem Monitoring Cooperative, Oktober 2021. http://dx.doi.org/10.18125/cc0a0l.
Der volle Inhalt der QuelleCalcagno, Juan Carlos, und Mariana Alfonso. Minority Enrollments at Public Universities of Diverse Selectivity Levels under Different Admission Regimes: The Case of Texas. Inter-American Development Bank, Oktober 2007. http://dx.doi.org/10.18235/0010878.
Der volle Inhalt der QuelleLinker, Taylor, und 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), Juli 2020. http://dx.doi.org/10.55274/r0011729.
Der volle Inhalt der QuelleBaxter, 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.), Februar 2024. http://dx.doi.org/10.21079/11681/48212.
Der volle Inhalt der QuelleLu, Tianjun, Jian-yu Ke, Fynnwin Prager und 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, August 2022. http://dx.doi.org/10.31979/mti.2022.2147.
Der volle Inhalt der QuelleMoreda, Fekadu, Benjamin Lord, Mauro Nalesso, Pedro Coli Valdes Daussa und Juliana Corrales. Hydro-BID: New Functionalities (Reservoir, Sediment and Groundwater Simulation Modules). Inter-American Development Bank, November 2016. http://dx.doi.org/10.18235/0009312.
Der volle Inhalt der QuelleHeifetz, Yael, und Michael Bender. Success and failure in insect fertilization and reproduction - the role of the female accessory glands. United States Department of Agriculture, Dezember 2006. http://dx.doi.org/10.32747/2006.7695586.bard.
Der volle Inhalt der QuelleAllen, Kathy, Andy Nadeau und Andy Robertston. Natural resource condition assessment: Salinas Pueblo Missions National Monument. National Park Service, Mai 2022. http://dx.doi.org/10.36967/nrr-2293613.
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