Literatura científica selecionada sobre o tema "Auc-Roc"
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Artigos de revistas sobre o assunto "Auc-Roc"
Hong, Chong Sun, e So Yeon Choi. "ROC curve generalization and AUC". Journal of the Korean Data And Information Science Society 31, n.º 4 (31 de julho de 2020): 477–88. http://dx.doi.org/10.7465/jkdi.2020.31.4.477.
Texto completo da fonteHong, Chong Sun, e Dae Soon Yang. "ROC curve and AUC for linear growth models". Journal of the Korean Data and Information Science Society 26, n.º 6 (30 de novembro de 2015): 1367–75. http://dx.doi.org/10.7465/jkdi.2015.26.6.1367.
Texto completo da fonteМинин, А. С. "Бинаризация вероятностного прогноза методом ROC AUC". ТЕНДЕНЦИИ РАЗВИТИЯ НАУКИ И ОБРАЗОВАНИЯ 104, n.º 14 (2023): 87–91. http://dx.doi.org/10.18411/trnio-12-2023-789.
Texto completo da fonteKrupinski, Elizabeth A. "Evaluating AI Clinically—It’s Not Just ROC AUC!" Radiology 298, n.º 1 (janeiro de 2021): 47–48. http://dx.doi.org/10.1148/radiol.2020203782.
Texto completo da fonteMukhametshin, Rustam F., Olga P. Kovtun e Nadezhda S. Davydova. "Respiratory parameters as a predictor of hospital outcomes in newborns requiring medical evacuation". Russian Journal of Pediatric Surgery, Anesthesia and Intensive Care 12, n.º 4 (19 de janeiro de 2023): 441–52. http://dx.doi.org/10.17816/psaic1292.
Texto completo da fonteMuschelli, John. "ROC and AUC with a Binary Predictor: a Potentially Misleading Metric". Journal of Classification 37, n.º 3 (23 de dezembro de 2019): 696–708. http://dx.doi.org/10.1007/s00357-019-09345-1.
Texto completo da fonteKhaidarov, A. G., A. I. Soloviev e D. A. Budko. "STUDY OF THE MOST EFFICIENT MODELS AND ATRIBUTION ALGORITHMS USING THE ROC AUC INDICATOR". Современные наукоемкие технологии (Modern High Technologies), n.º 7 2022 (2022): 63–68. http://dx.doi.org/10.17513/snt.39234.
Texto completo da fonteGarcía de Guadiana-Romualdo, Luis, María Dolores Albaladejo-Otón, Mario Berger, Enrique Jiménez-Santos, Roberto Jiménez-Sánchez, Patricia Esteban-Torrella, Sergio Rebollo-Acebes, Ana Hernando-Holgado, Alejandro Ortín-Freire e Javier Trujillo-Santos. "Prognostic performance of pancreatic stone protein in critically ill patients with sepsis". Biomarkers in Medicine 13, n.º 17 (dezembro de 2019): 1469–80. http://dx.doi.org/10.2217/bmm-2019-0174.
Texto completo da fonteSauka, Kudzai, Gun-Yoo Shin, Dong-Wook Kim e Myung-Mook Han. "Adversarial Robust and Explainable Network Intrusion Detection Systems Based on Deep Learning". Applied Sciences 12, n.º 13 (25 de junho de 2022): 6451. http://dx.doi.org/10.3390/app12136451.
Texto completo da fonteAmala, R., e Sudesh Pundir. "ROC Curve and AUC for A Left-Truncated Sample from Rayleigh Distribution". American Journal of Mathematical and Management Sciences 34, n.º 2 (31 de dezembro de 2014): 89–116. http://dx.doi.org/10.1080/01966324.2014.969461.
Texto completo da fonteTeses / dissertações sobre o assunto "Auc-Roc"
Zheng, Shimin. "The ROC Curve and the Area under the Curve (AUC)". Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etsu-works/139.
Texto completo da fonteLu, Qing. "Methods for Designing and Forming Predictive Genetic Tests". Case Western Reserve University School of Graduate Studies / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1212197560.
Texto completo da fonteYuan, Yan. "Empirical Likelihood-Based NonParametric Inference for the Difference between Two Partial AUCS". Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/math_theses/32.
Texto completo da fonteHuang, Xin. "Bootstrap and Empirical Likelihood-based Semi-parametric Inference for the Difference between Two Partial AUCs". Digital Archive @ GSU, 2008. http://digitalarchive.gsu.edu/math_theses/54.
Texto completo da fonteSun, Fangfang. "Semi-parametric inference for the partial area under the ROC curve". unrestricted, 2008. http://etd.gsu.edu/theses/available/etd-11192008-113213/.
Texto completo da fonteTitle from file title page. Gengsheng Qin, committee chair; Yu-Sheng Hsu, Yixin Fang, Yuanhui Xiao, committee members. Description based on contents viewed July 22, 2009. Includes bibliographical references (p. 29-30).
Zhou, Haochuan. "Statistical Inferences for the Youden Index". Digital Archive @ GSU, 2011. http://digitalarchive.gsu.edu/math_diss/5.
Texto completo da fonteXu, Ping. "Evaluation of Repeated Biomarkers: Non-parametric Comparison of Areas under the Receiver Operating Curve Between Correlated Groups Using an Optimal Weighting Scheme". Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/4261.
Texto completo da fonteBitara, Matúš. "Srovnání heuristických a konvenčních statistických metod v data miningu". Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2019. http://www.nusl.cz/ntk/nusl-400833.
Texto completo da fonteKhamesipour, Alireza. "IMPROVED GENE PAIR BIOMARKERS FOR MICROARRAY DATA CLASSIFICATION". OpenSIUC, 2018. https://opensiuc.lib.siu.edu/dissertations/1573.
Texto completo da fonteWang, Binhuan. "Statistical Evaluation of Continuous-Scale Diagnostic Tests with Missing Data". Digital Archive @ GSU, 2012. http://digitalarchive.gsu.edu/math_diss/8.
Texto completo da fonteCapítulos de livros sobre o assunto "Auc-Roc"
Klawonn, Frank, Frank Höppner e Sigrun May. "An Alternative to ROC and AUC Analysis of Classifiers". In Advances in Intelligent Data Analysis X, 210–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24800-9_21.
Texto completo da fonteGentili, Elisabetta, Alice Bizzarri, Damiano Azzolini, Riccardo Zese e Fabrizio Riguzzi. "Regularization in Probabilistic Inductive Logic Programming". In Inductive Logic Programming, 16–29. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49299-0_2.
Texto completo da fonteMarcus, Pamela M. "Performance Measures". In Assessment of Cancer Screening, 23–38. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94577-0_3.
Texto completo da fonteKoncar, Philipp, e Denis Helic. "Employee Satisfaction in Online Reviews". In Lecture Notes in Computer Science, 152–67. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60975-7_12.
Texto completo da fonteFeretzakis, Georgios, Aikaterini Sakagianni, Evangelos Loupelis, Dimitris Kalles, Vasileios Panteris, Lazaros Tzelves, Rea Chatzikyriakou et al. "Prediction of Hospitalization Using Machine Learning for Emergency Department Patients". In Studies in Health Technology and Informatics. IOS Press, 2022. http://dx.doi.org/10.3233/shti220422.
Texto completo da fonteKyparissidis Kokkinidis, Ilias, Evangelos Logaras, Emmanouil S. Rigas, Ioannis Tsakiridis, Themistoklis Dagklis, Antonis Billis e Panagiotis D. Bamidis. "Towards an Explainable AI-Based Tool to Predict Preterm Birth". In Caring is Sharing – Exploiting the Value in Data for Health and Innovation. IOS Press, 2023. http://dx.doi.org/10.3233/shti230207.
Texto completo da fonteSakagianni, Aikaterini, Christina Koufopoulou, Vassilios Verykios, Evangelos Loupelis, Dimitrios Kalles e Georgios Feretzakis. "Prediction of COVID-19 Mortality in the Intensive Care Unit Using Machine Learning". In Caring is Sharing – Exploiting the Value in Data for Health and Innovation. IOS Press, 2023. http://dx.doi.org/10.3233/shti230200.
Texto completo da fonteLakshmi Shree K. e Ashok Kumar R. "Global Events to Enhance Tourism". In Advances in Marketing, Customer Relationship Management, and E-Services, 66–86. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-6591-2.ch005.
Texto completo da fonteSweetnich, Stephen R., e David R. Jacques. "Skin Detection With Small Unmanned Aerial Systems by Integration of Area Scan Multispectral Imagers and Factors Affecting Their Design and Operation". In Unmanned Aerial Vehicles, 215–34. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8365-3.ch009.
Texto completo da fonteFigueirêdo, Ilan, Lílian Lefol Nani Guarieiro e Erick Giovani Sperandio Nascimento. "Multivariate Real Time Series Data Using Six Unsupervised Machine Learning Algorithms". In Anomaly Detection - Recent Advances, Issues and Challenges [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.94944.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Auc-Roc"
Hong, Shenda, Cao Xiao, Trong Nghia Hoang, Tengfei Ma, Hongyan Li e Jimeng Sun. "RDPD: Rich Data Helps Poor Data via Imitation". In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/817.
Texto completo da fonteShekter, Dylan H., e Frank W. Samuelson. "Efficiently calculating ROC curves, AUC, and uncertainty from 2AFC studies with finite samples". In Image Perception, Observer Performance, and Technology Assessment, editado por Frank W. Samuelson e Sian Taylor-Phillips. SPIE, 2020. http://dx.doi.org/10.1117/12.2550601.
Texto completo da fonteFerris, Michael H., Michael McLaughlin, Samuel Grieggs, Soundararajan Ezekiel, Erik Blasch, Mark Alford, Maria Cornacchia e Adnan Bubalo. "Using ROC curves and AUC to evaluate performance of no-reference image fusion metrics". In NAECON 2015 - IEEE National Aerospace and Electronics Conference. IEEE, 2015. http://dx.doi.org/10.1109/naecon.2015.7443034.
Texto completo da fonteChaves, Rubens Marques, André Luis Debiaso Rossi e Luís Paulo Faina Garcia. "A Financial Distress Prediction using a Non-stationary Dataset". In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/eniac.2023.234013.
Texto completo da fonteRodrigues, Gustavo, e Diego Kreutz. "Modelo preditivo para classificação de risco de óbito de pacientes com COVID-19 utilizando dados abertos". In Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/sbcas.2022.222494.
Texto completo da fonteCalheiros, José, Lucas Amorim, Lucas Lima e Marcelo Oliveira. "Os efeitos da utilização de atributos perinodulares na classificação de nódulos pulmonares". In Anais Principais do Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/sbcas.2020.11510.
Texto completo da fonteYunisa, Regina, e Freddy Haryanto. "Sensitivity and accuracy analysis of CT image in PRISM autocontouring using confusion matrix and ROC/AUC curve methods". In THE 5TH INTERNATIONAL CONFERENCE ON MATHEMATICS AND NATURAL SCIENCES. AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4930656.
Texto completo da fonteAlam, S., O. Olabiyi, O. Odejide e A. Annamalai. "Energy detector's performance evaluation in a relay based cognitive radio network: Area under the ROC curve (AUC) approach". In 2011 IEEE Globecom Workshops. IEEE, 2011. http://dx.doi.org/10.1109/glocomw.2011.6162466.
Texto completo da fonteAmagada, P. U. "An Inferable Machine Learning Approach for Reservoir Lithology Characterization Using Drilling Data". In SPE Annual Technical Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/217485-stu.
Texto completo da fonteF. Machado, Giovani, Luciana F. Almeida e Juan G. Lazo Lazo. "Técnicas de Aprendizado de Máquina para Previsão de Perdas Severas em Rochas Carbonáticas de Reservatórios Do Pré-Sal". In Congresso Brasileiro de Automática - 2020. sbabra, 2020. http://dx.doi.org/10.48011/asba.v2i1.1264.
Texto completo da fonteRelatórios de organizações sobre o assunto "Auc-Roc"
Tayeb, Shahab. Taming the Data in the Internet of Vehicles. Mineta Transportation Institute, janeiro de 2022. http://dx.doi.org/10.31979/mti.2022.2014.
Texto completo da fonteChen, Xiaole, Peng Wang, Yunquan Luo, Yi-Yu Lu, Wenjun Zhou, Mengdie Yang, Jian Chen, Zhi-Qiang Meng e Shi-Bing Su. Therapeutic Efficacy Evaluation and Underlying Mechanisms Prediction of Jianpi Liqi Decoction for Hepatocellular Carcinoma. Science Repository, setembro de 2021. http://dx.doi.org/10.31487/j.jso.2021.02.04.sup.
Texto completo da fonte