Análisis de calibración en modelos de aprendizaje de máquina cuántico

El análisis de calibración de modelos de aprendizaje de máquina cobra gran importancia en distintos contextos como evaluación del riesgo, diagnósticos y sistemas críticos para la seguridad, donde hay decisiones influenciadas por las predicciones de los modelos. El área del aprendizaje de máquina cuá...

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Autores:: Amaya Cruz, Glenn Harry

Tipo de recurso:

Fecha de publicación:: 2023

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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repository_id_str
dc.title.spa.fl_str_mv	Análisis de calibración en modelos de aprendizaje de máquina cuántico
dc.title.translated.eng.fl_str_mv	Calibration analysis in quantum machine learning models
title	Análisis de calibración en modelos de aprendizaje de máquina cuántico
spellingShingle	Análisis de calibración en modelos de aprendizaje de máquina cuántico 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería 620 - Ingeniería y operaciones afines::621 - Física aplicada Evaluación de riesgos Teoría del campo cuántico Risk assessment Quantum field theory Aprendizaje de máquina Aprendizaje de máquina cuántico Calibración Análisis de confianza Machine learning Quantum machine learning Calibration Confident analysis
title_short	Análisis de calibración en modelos de aprendizaje de máquina cuántico
title_full	Análisis de calibración en modelos de aprendizaje de máquina cuántico
title_fullStr	Análisis de calibración en modelos de aprendizaje de máquina cuántico
title_full_unstemmed	Análisis de calibración en modelos de aprendizaje de máquina cuántico
title_sort	Análisis de calibración en modelos de aprendizaje de máquina cuántico
dc.creator.fl_str_mv	Amaya Cruz, Glenn Harry
dc.contributor.advisor.none.fl_str_mv	Gonzalez Osorio, Fabio Augusto Toledo Cortés, Santiago
dc.contributor.author.none.fl_str_mv	Amaya Cruz, Glenn Harry
dc.contributor.researchgroup.spa.fl_str_mv	Mindlab
dc.subject.ddc.spa.fl_str_mv	620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería 620 - Ingeniería y operaciones afines::621 - Física aplicada
topic	620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería 620 - Ingeniería y operaciones afines::621 - Física aplicada Evaluación de riesgos Teoría del campo cuántico Risk assessment Quantum field theory Aprendizaje de máquina Aprendizaje de máquina cuántico Calibración Análisis de confianza Machine learning Quantum machine learning Calibration Confident analysis
dc.subject.lemb.spa.fl_str_mv	Evaluación de riesgos Teoría del campo cuántico
dc.subject.lemb.eng.fl_str_mv	Risk assessment Quantum field theory
dc.subject.proposal.spa.fl_str_mv	Aprendizaje de máquina Aprendizaje de máquina cuántico Calibración Análisis de confianza
dc.subject.proposal.eng.fl_str_mv	Machine learning Quantum machine learning Calibration Confident analysis
description	El análisis de calibración de modelos de aprendizaje de máquina cobra gran importancia en distintos contextos como evaluación del riesgo, diagnósticos y sistemas críticos para la seguridad, donde hay decisiones influenciadas por las predicciones de los modelos. El área del aprendizaje de máquina cuántico ha recibido una mayor atención en los últimos años, en particular, se han desarrollado modelos que obtienen resultados competitivos en tareas de clasificación y regresión a comparación con métodos ampliamente utilizados. No obstante, las propiedades de este tipo de clasificadores en términos de calibración no han sido exploradas en la literatura. Por esta razón, en el presente trabajo se realiza un estudio de las propiedades de calibración que tienen algunos modelos de aprendizaje de máquina cuántico frente a modelos ampliamente usados en la literatura como máquinas de soporte vectorial, árboles de decisión, regresión logística, entre otros para tareas de clasificación binaria y de múltiples clases. Adicionalmente, se realiza un experimento para explorar el efecto de algunos clasificadores cuánticos en combinación con una red neuronal. Los resultados experimentales muestran que algunos de los clasificadores cuánticos analizados tienen un rendimiento competitivo e incluso mejor en métricas de calibración y las tareas de clasificación. (texto tomado de la fuente)
publishDate	2023
dc.date.accessioned.none.fl_str_mv	2023-04-18T22:42:58Z
dc.date.available.none.fl_str_mv	2023-04-18T22:42:58Z
dc.date.issued.none.fl_str_mv	2023
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/83732
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/83732 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	Ayhan, Murat S. ; Berens, Philipp: Test-time data augmentation for estimation of heteroscedastic aleatoric uncertainty in deep neural networks. In: Medical Imaging with Deep Learning (MIDL), 2018, S. 1–9 Aha, D ; Kibler, Dennis: Instance-based prediction of heart-disease presence with the Cleveland database. University of California 3 (1988), Nr. 1, S. 3–2 Bastola, S. ; Ishidaira, H. ; Takeuchi, K.: Regionalisation of hydrological model parameters under parameter uncertainty: A case study involving TOPMODEL and basins across the globe. Journal of Hydrology 357 (2008), Nr. 3-4, S. 188–206 Beaudouin, R. ; Monod, G. ; Ginot, V.: Selecting parameters for calibration via sensitivity analysis: An individual-based model of mosquitofish population dynamics. Ecological Modelling 218 (2008), Nr. 1-2, S. 29–48 Brier, Glenn W.: Verification of forecast expressed in terms of probability. Monthly Weather Review 78 (1950), jan, Nr. 1, S. 1–3. – ISSN 0027–0644 Bröcker, Jochen ; Smith, Leonard A.: Increasing the reliability of reliability diagrams. Weather and Forecasting 22 (2007), jun, Nr. 3, S. 651–661. – ISSN 08828156 Biamonte, Jacob ; Wittek, Peter ; Pancotti, Nicola ; Rebentrost, Patrick ; Wiebe, Nathan ; Lloyd, Seth: Quantum machine learning. Nature 549 (2017), Nr. 7671, S. 195–202 Chollet, Fran¸cois ; others. Keras. https://keras.io. 2015 Charoenpanyanet, A.: Modeling Anopheles mosquito density spatial and seasonal variations using remotely sensed imagery and statistical methods. International Journal of Geoinformatics 13 (2017), Nr. 1, S. 35–47 Carneiro, G. ; Zorron Cheng Tao Pu, L. ; Singh, R. ; Burt, A.: Deep learning uncertainty and confidence calibration for the five-class polyp classification from colonoscopy. Medical Image Analysis 62 (2020) Degroot, M. D. ; Fienberg, Stephen E. The comparison and evaluation of forecasters. mar 1982 Dua, Dheeru ; Graff, Casey. UCI Machine Learning Repository. 2017 Demiroz, G ; Govenir, HA ; Ilter, N: Learning differential diagnosis of eryhematosquamous diseases using voting feature intervals. Artificial Intelligence in Medicine 13 (1998), Nr. 3, S. 147–165 Diego Hernando, Useche R.: Quantum measurement learning for medical image classification. (2022) Detrano, Robert ; Janosi, Andras ; Steinbrunn, Walter ; Pfisterer, Matthias ; Schmid, Johann-Jakob ; Sandhu, Sarbjit ; Guppy, Kern H. ; Lee, Stella ; Froelicher, Victor: International application of a new probability algorithm for the diagnosis of coronary artery disease. The American journal of cardiology 64 (1989), Nr. 5, S. 304–310 Dormann, Carsten F.: Calibration of probability predictions from machine-learning and statistical models. Global Ecology and Biogeography 29 (2020), apr, Nr. 4, S. 760–765. – ISSN 14668238 Deng, Y. ; Yang, B.-R. ; Luo, J.-W. ; Du, G.-X. ; Luo, L.-P.: DTI-based radiomics signature for the detection of early diabetic kidney damage. Abdominal Radiology 45 (2020), Nr. 8, S. 2526–2531 Feng, Runhai: Improving uncertainty analysis in well log classification by machine learning with a scaling algorithm. Journal of Petroleum Science and Engineering 196 (2021). – ISSN 09204105 Foster, Dean P. ; Vohra, Rakesh V.: Asymptotic calibration. Biometrika 85 (1998), Nr. 2, S. 379–390. – ISSN 00063444 Galindo, Y. ; De Cicco, M. ; Quiles, M.G. ; Lorena, A.C.: Monitoring Night Skies with Deep Learning. Bd. 1332. 2020 460–468 Seiten. – ISBN 9783030638191 González, Fabio A. ; Gallego, Alejandro ; Toledo-Cortés, Santiago ; Vargas-Calderón, Vladimir: Learning with Density Matrices and Random Features. arXiv preprint arXiv:2102.04394 (2021) Gallego-Mejia, Joseph ; Bustos-Brinez, Oscar ; Gonzalez, Fabio: InQMAD: Incremental Quantum Measurement Anomaly Detection. arXiv preprint arXiv:2210.05061 (2022) Glasser, Ivan ; Pancotti, Nicola ; Ignacio Cirac, J.: From Probabilistic Graphical Models to Generalized Tensor Networks for Supervised Learning. IEEE Access 8 (2020), S. 68169–68182. – ISSN 21693536 Guo, Chuan ; Pleiss, Geoff ; Sun, Yu ; Weinberger, Kilian Q. On calibration of modern neural networks. 2017 Gupta, Kartik ; Rahimi, Amir ; Ajanthan, Thalaiyasingam ; Mensink, Thomas ; Sminchisescu, Cristian ; Hartley, Richard: Calibration of neural networks using splines. arXiv preprint arXiv:2006.12800 (2020), 6 Guan, Yawen ; Sampson, Christian ; Tucker, J. D. ; Chang, Won ; Mondal, Anirban ; Haran, Murali ; Sulsky, Deborah: Computer Model Calibration Based on ImageWarping Metrics: An Application for Sea Ice Deformation. Journal of Agricultural, Biological, and Environmental Statistics 24 (2019), Nr. 3, S. 444–463. – ISSN 15372693 Ghoshal, Biraja ; Tucker, Allan: On calibrated model uncertainty in deep learning. arXiv preprint arXiv:2206.07795 (2022) González, Fabio A. ; Vargas-Calderón, Vladimir ; Vinck-Posada, Herbert: Classification with Quantum Measurements. Journal of the Physical Society of Japan 90 (2021), Nr. 4, S. 044002 Higdon, Dave ; Gattiker, James ; Williams, Brian ; Rightley, Maria: Computer model calibration using high-dimensional output. Journal of the American Statistical Association 103 (2008), Nr. 482, S. 570–583. – ISSN 01621459 Iliyasu, Abdullah M. ; Fatichah, Chastine: A quantum hybrid PSO combined with fuzzy k-NN approach to feature selection and cell classification in cervical cancer detection. Sensors (Switzerland) 17 (2017), Nr. 12. – ISSN 14248220 Jensen, M.H. ; Jørgensen, D.R. ; Jalaboi, R. ; Hansen, M.E. ; Olsen, M.A.: Improving uncertainty estimation in convolutional neural networks using inter-rater agreement. Bd. 11767 LNCS. 2019 540–548 Seiten. – ISBN 9783030322502 Jiang, Xiaoqian ; Osl, Melanie ; Kim, Jihoon ; Ohno-Machado, Lucila: Calibrating predictive model estimates to support personalized medicine. Journal of the American Medical Informatics Association 19 (2012), mar, Nr. 2, S. 263–274. – ISSN 10675027 Kuleshov, Volodymyr ; Ermon, Stefano: Estimating uncertainty online against an adversary. In: 31st AAAI Conference on Artificial Intelligence, AAAI 2017 , 2017, S. 2110–2116 Krizhevsky, Alex ; Hinton, Geoffrey ; others: Learning multiple layers of features from tiny images. (2009) Kuhn, Max ; Johnson, Kjell: Applied predictive modeling. Bd. 26. Springer, 2013 Kuleshov, Volodymyr ; Liang, Percy: Calibrated structured prediction. In: Advances in Neural Information Processing Systems Bd. 2015-Janua. Bd. 2015-Janua, 2015. ISSN 10495258, S. 3474–3482 Kumar, Ananya ; Liang, Percy S. ; Ma, Tengyu: Verified uncertainty calibration. Advances in Neural Information Processing Systems 32 (2019) Kull, Meelis ; Perello Nieto, Miquel ; K¨angsepp, Markus ; Silva Filho, Telmo ; Song, Hao ; Flach, Peter. Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with dirichlet calibration. 2019 Müller, R. ; Kornblith, S. ; Hinton, G.: When does label smoothing help? In: Advances in Neural Information Processing Systems Bd. 32. Bd. 32, 2019 Naeini, Mahdi P. ; Cooper, Gregory ; Hauskrecht, Milos: Obtaining well calibrated probabilities using bayesian binning. In: Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015 Nixon, Jeremy ; Dusenberry, Michael W. ; Zhang, Linchuan ; Jerfel, Ghassen ; Tran, Dustin: Measuring Calibration in Deep Learning. 2 (2019), Nr. 7 Niculescu-Mizil, Alexandru ; Caruana, Rich: Predicting good probabilities with supervised learning. In: ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning, 2005. – ISBN 1595931805, S. 625–632 Nguyen, Khanh ; O’Connor, Brendan: Posterior calibration and exploratory analysis for natural language processing models. In: Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing, 2015. – ISBN 9781941643327, S. 1587–1598 Posocco, Nicolas ; Bonnefoy, Antoine: Estimating Expected Calibration Errors. In: International Conference on Artificial Neural Networks Springer, 2021, S. 139–150 Peleg, K.: Fast fourier transform based calibration in remote sensing. International Journal of Remote Sensing 19 (1998), Nr. 12, S. 2301–2315 Pearce, Jennie ; Ferrier, Simon: Evaluating the predictive performance of habitat models developed using logistic regression. Ecological Modelling 133 (2000), Nr. 3, S. 225–245. – ISSN 03043800 Platt, John ; Others: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in large margin classifiers 10 (1999), Nr. 3, S. 61–74 Pedregosa, F. ; Varoquaux, G. ; Gramfort, A. ; Michel, V. ; Thirion, B. ; Grisel, O. ; Blondel, M. ; Prettenhofer, P. ; Weiss, R. ; Dubourg, V. ; Vanderplas, J. ; Passos, A. ; Cournapeau, D. ; Brucher, M. ; Perrot, M. ; Duchesnay, E.: Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12 (2011), S. 2825–2830 Sergioli, Giuseppe ; Militello, Carmelo ; Rundo, Leonardo ; Minafra, Luigi ; Torrisi, Filippo ; Russo, Giorgio ; Chow, Keng L. ; Giuntini, Roberto: A quantum-inspired classifier for clonogenic assay evaluations. Scientific Reports 11 (2021), Nr. 1, S. 2830. – ISBN 0123456789 Schuld, Maria ; Sinayskiy, Ilya ; Petruccione, Francesco: An introduction to quantum machine learning. Contemporary Physics 56 (2015), Nr. 2, S. 172–185. – ISSN 13665812 Toledo-Cortés, Santiago ; Useche, Diego H. ; González, Fabio A.: Prostate Tissue Grading with Deep Quantum Measurement Ordinal Regression. arXiv preprint arXiv:2103.03188 (2021) Torres-Meza, M.d.J. ; Báez-González, A.D. ; Maciel-Pérez, L.H. ; Quezada-Guzmán, E. ; Sierra-Tristán, J.S.: GIS-based modeling of the geographic distribution of Quercus emoryi Torr. (Fagaceae) in México and identification of significant environmental factors influencing the species’ distribution. Ecological Modelling 220 (2009), Nr. 24, S. 3599– 3611 Vaicenavicius, Juozas ; Widmann, David ; Andersson, Carl ; Lindsten, Fredrik ; Roll, Jacob ; Sch¨on, Thomas: Evaluating model calibration in classification. In: The 22nd International Conference on Artificial Intelligence and Statistics PMLR, 2019, S. 3459– 3467 Widmann, David ; Lindsten, Fredrik ; Zachariah, Dave: Calibration tests in multiclass classification: A unifying framework. Advances in Neural Information Processing Systems 32 (2019) Zadrozny, Bianca ; Elkan, Charles: Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers. Icml (2001), S. 1–8. ISBN 1–55860–778–1 Zadrozny, Bianca ; Elkan, Charles: Transforming classifier scores into accurate multiclass probability estimates. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2002. – ISBN 158113567X, S. 694–699 LeCun, Yann ; Bottou, Léon ; Bengio, Yoshua ; Haffner, Patrick: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86 (1998), Nr. 11, S. 2278–2324
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dc.rights.license.spa.fl_str_mv	Atribución-NoComercial 4.0 Internacional
dc.rights.uri.spa.fl_str_mv	http://creativecommons.org/licenses/by-nc/4.0/
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dc.format.extent.spa.fl_str_mv	xiii, 53 páginas
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y Computación
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ingeniería
dc.publisher.place.spa.fl_str_mv	Bogotá,Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
institution	Universidad Nacional de Colombia
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spelling	Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Gonzalez Osorio, Fabio Augusto0e9d70b5c1d7448338ca4467ccb27e59Toledo Cortés, Santiagoaacc1c99e2c7e404d2f99a7a954b57c8Amaya Cruz, Glenn Harryb0536d7a878b2132355a7f85bc1c017bMindlab2023-04-18T22:42:58Z2023-04-18T22:42:58Z2023https://repositorio.unal.edu.co/handle/unal/83732Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/El análisis de calibración de modelos de aprendizaje de máquina cobra gran importancia en distintos contextos como evaluación del riesgo, diagnósticos y sistemas críticos para la seguridad, donde hay decisiones influenciadas por las predicciones de los modelos. El área del aprendizaje de máquina cuántico ha recibido una mayor atención en los últimos años, en particular, se han desarrollado modelos que obtienen resultados competitivos en tareas de clasificación y regresión a comparación con métodos ampliamente utilizados. No obstante, las propiedades de este tipo de clasificadores en términos de calibración no han sido exploradas en la literatura. Por esta razón, en el presente trabajo se realiza un estudio de las propiedades de calibración que tienen algunos modelos de aprendizaje de máquina cuántico frente a modelos ampliamente usados en la literatura como máquinas de soporte vectorial, árboles de decisión, regresión logística, entre otros para tareas de clasificación binaria y de múltiples clases. Adicionalmente, se realiza un experimento para explorar el efecto de algunos clasificadores cuánticos en combinación con una red neuronal. Los resultados experimentales muestran que algunos de los clasificadores cuánticos analizados tienen un rendimiento competitivo e incluso mejor en métricas de calibración y las tareas de clasificación. (texto tomado de la fuente)Calibration of machine learning models is of great importance in different contexts such as risk assessment, diagnostics, and safety-critical systems, in which decisions are influenced by model predictions. The area of quantum machine learning has received an increased attention in recent years, in particular, models have been developed that obtain competitive results in classification and regression tasks compared to widely used methods. However, the properties of this type of classifiers in terms of calibration have not been explored in the literature. As a result, in this work a study of the properties of calibration is conducted for recent quantum machine learning models in comparison to state-of-the-art models such as support vector machines, decisions trees, logistic regression, and others for binary and multiclass classification tasks. Moreover, an experiment to explore the effect of some quantum classifiers in combination with a neural network is made. The experimental results show that some of the analyzed quantum classifiers have competitive and even better performance in calibration metrics and the classification tasks.MaestríaMagíster en Ingeniería - Ingeniería de Sistemas y ComputaciónSistemas Inteligentesxiii, 53 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y ComputaciónFacultad de IngenieríaBogotá,ColombiaUniversidad Nacional de Colombia - Sede Bogotá620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería620 - Ingeniería y operaciones afines::621 - Física aplicadaEvaluación de riesgosTeoría del campo cuánticoRisk assessmentQuantum field theoryAprendizaje de máquinaAprendizaje de máquina cuánticoCalibraciónAnálisis de confianzaMachine learningQuantum machine learningCalibrationConfident analysisAnálisis de calibración en modelos de aprendizaje de máquina cuánticoCalibration analysis in quantum machine learning modelsTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAyhan, Murat S. ; Berens, Philipp: Test-time data augmentation for estimation of heteroscedastic aleatoric uncertainty in deep neural networks. In: Medical Imaging with Deep Learning (MIDL), 2018, S. 1–9Aha, D ; Kibler, Dennis: Instance-based prediction of heart-disease presence with the Cleveland database. University of California 3 (1988), Nr. 1, S. 3–2Bastola, S. ; Ishidaira, H. ; Takeuchi, K.: Regionalisation of hydrological model parameters under parameter uncertainty: A case study involving TOPMODEL and basins across the globe. Journal of Hydrology 357 (2008), Nr. 3-4, S. 188–206Beaudouin, R. ; Monod, G. ; Ginot, V.: Selecting parameters for calibration via sensitivity analysis: An individual-based model of mosquitofish population dynamics. Ecological Modelling 218 (2008), Nr. 1-2, S. 29–48Brier, Glenn W.: Verification of forecast expressed in terms of probability. Monthly Weather Review 78 (1950), jan, Nr. 1, S. 1–3. – ISSN 0027–0644Bröcker, Jochen ; Smith, Leonard A.: Increasing the reliability of reliability diagrams. Weather and Forecasting 22 (2007), jun, Nr. 3, S. 651–661. – ISSN 08828156Biamonte, Jacob ; Wittek, Peter ; Pancotti, Nicola ; Rebentrost, Patrick ; Wiebe, Nathan ; Lloyd, Seth: Quantum machine learning. Nature 549 (2017), Nr. 7671, S. 195–202Chollet, Fran¸cois ; others. Keras. https://keras.io. 2015Charoenpanyanet, A.: Modeling Anopheles mosquito density spatial and seasonal variations using remotely sensed imagery and statistical methods. International Journal of Geoinformatics 13 (2017), Nr. 1, S. 35–47Carneiro, G. ; Zorron Cheng Tao Pu, L. ; Singh, R. ; Burt, A.: Deep learning uncertainty and confidence calibration for the five-class polyp classification from colonoscopy. Medical Image Analysis 62 (2020)Degroot, M. D. ; Fienberg, Stephen E. The comparison and evaluation of forecasters. mar 1982Dua, Dheeru ; Graff, Casey. UCI Machine Learning Repository. 2017Demiroz, G ; Govenir, HA ; Ilter, N: Learning differential diagnosis of eryhematosquamous diseases using voting feature intervals. Artificial Intelligence in Medicine 13 (1998), Nr. 3, S. 147–165Diego Hernando, Useche R.: Quantum measurement learning for medical image classification. (2022)Detrano, Robert ; Janosi, Andras ; Steinbrunn, Walter ; Pfisterer, Matthias ; Schmid, Johann-Jakob ; Sandhu, Sarbjit ; Guppy, Kern H. ; Lee, Stella ; Froelicher, Victor: International application of a new probability algorithm for the diagnosis of coronary artery disease. The American journal of cardiology 64 (1989), Nr. 5, S. 304–310Dormann, Carsten F.: Calibration of probability predictions from machine-learning and statistical models. Global Ecology and Biogeography 29 (2020), apr, Nr. 4, S. 760–765. – ISSN 14668238Deng, Y. ; Yang, B.-R. ; Luo, J.-W. ; Du, G.-X. ; Luo, L.-P.: DTI-based radiomics signature for the detection of early diabetic kidney damage. Abdominal Radiology 45 (2020), Nr. 8, S. 2526–2531Feng, Runhai: Improving uncertainty analysis in well log classification by machine learning with a scaling algorithm. Journal of Petroleum Science and Engineering 196 (2021). – ISSN 09204105Foster, Dean P. ; Vohra, Rakesh V.: Asymptotic calibration. Biometrika 85 (1998), Nr. 2, S. 379–390. – ISSN 00063444Galindo, Y. ; De Cicco, M. ; Quiles, M.G. ; Lorena, A.C.: Monitoring Night Skies with Deep Learning. Bd. 1332. 2020 460–468 Seiten. – ISBN 9783030638191González, Fabio A. ; Gallego, Alejandro ; Toledo-Cortés, Santiago ; Vargas-Calderón, Vladimir: Learning with Density Matrices and Random Features. arXiv preprint arXiv:2102.04394 (2021)Gallego-Mejia, Joseph ; Bustos-Brinez, Oscar ; Gonzalez, Fabio: InQMAD: Incremental Quantum Measurement Anomaly Detection. arXiv preprint arXiv:2210.05061 (2022)Glasser, Ivan ; Pancotti, Nicola ; Ignacio Cirac, J.: From Probabilistic Graphical Models to Generalized Tensor Networks for Supervised Learning. 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Proceedings of the IEEE 86 (1998), Nr. 11, S. 2278–2324EstudiantesInvestigadoresTHUMBNAIL1022435940.2023.pdf.jpg1022435940.2023.pdf.jpgGenerated Thumbnailimage/jpeg4143https://repositorio.unal.edu.co/bitstream/unal/83732/3/1022435940.2023.pdf.jpgace460b546c6a0a85559b2564debbd49MD53LICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/83732/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1022435940.2023.pdf1022435940.2023.pdfTesis de Maestría en Ingeniería de Sistemasapplication/pdf849061https://repositorio.unal.edu.co/bitstream/unal/83732/2/1022435940.2023.pdf49cf69b064dbf288134a811a13560c0dMD52unal/83732oai:repositorio.unal.edu.co:unal/837322023-08-02 23:03:57.201Repositorio Institucional Universidad Nacional de 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Análisis de calibración en modelos de aprendizaje de máquina cuántico

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