Academic literature on the topic 'Leave one out method'
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Journal articles on the topic "Leave one out method"
Krzanowski, W. J., and D. J. Hand. "ASSESSING ERROR RATE ESTIMATORS: THE LEAVE-ONE-OUT METHOD RECONSIDERED." Australian Journal of Statistics 39, no. 1 (March 1997): 35–46. http://dx.doi.org/10.1111/j.1467-842x.1997.tb00521.x.
Full textZhang, Tong. "Leave-One-Out Bounds for Kernel Methods." Neural Computation 15, no. 6 (June 1, 2003): 1397–437. http://dx.doi.org/10.1162/089976603321780326.
Full textLi, Xiping, David Tripe, Chris Malone, and David Smith. "Measuring systemic risk contribution: The leave-one-out z-score method." Finance Research Letters 36 (October 2020): 101316. http://dx.doi.org/10.1016/j.frl.2019.101316.
Full textBrovelli, Maria Antonia, Mattia Crespi, Francesca Fratarcangeli, Francesca Giannone, and Eugenio Realini. "Accuracy assessment of high resolution satellite imagery orientation by leave-one-out method." ISPRS Journal of Photogrammetry and Remote Sensing 63, no. 4 (July 2008): 427–40. http://dx.doi.org/10.1016/j.isprsjprs.2008.01.006.
Full textALPTEKIN, AHMET, and OLCAY KURSUN. "MISS ONE OUT: A CROSS-VALIDATION METHOD UTILIZING INDUCED TEACHER NOISE." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 07 (November 2013): 1351003. http://dx.doi.org/10.1142/s0218001413510038.
Full textMontoya Perez, Ileana, Antti Airola, Peter J. Boström, Ivan Jambor, and Tapio Pahikkala. "Tournament leave-pair-out cross-validation for receiver operating characteristic analysis." Statistical Methods in Medical Research 28, no. 10-11 (August 20, 2018): 2975–91. http://dx.doi.org/10.1177/0962280218795190.
Full textBelotti, Federico, and Franco Peracchi. "Fast leave-one-out methods for inference, model selection, and diagnostic checking." Stata Journal: Promoting communications on statistics and Stata 20, no. 4 (December 2020): 785–804. http://dx.doi.org/10.1177/1536867x20976312.
Full textLee, M. M. S., S. S. Keerthi, C. J. Ong, and D. DeCoste. "An Efficient Method for Computing Leave-One-Out Error in Support Vector Machines With Gaussian Kernels." IEEE Transactions on Neural Networks 15, no. 3 (May 2004): 750–57. http://dx.doi.org/10.1109/tnn.2004.824266.
Full textBo, Liefeng, Ling Wang, and Licheng Jiao. "Feature Scaling for Kernel Fisher Discriminant Analysis Using Leave-One-Out Cross Validation." Neural Computation 18, no. 4 (April 1, 2006): 961–78. http://dx.doi.org/10.1162/neco.2006.18.4.961.
Full textLv, Liye, Xueguan Song, and Wei Sun. "Modify Leave-One-Out Cross Validation by Moving Validation Samples around Random Normal Distributions: Move-One-Away Cross Validation." Applied Sciences 10, no. 7 (April 3, 2020): 2448. http://dx.doi.org/10.3390/app10072448.
Full textDissertations / Theses on the topic "Leave one out method"
Tseng, Hsin-Wu, Jiahua Fan, and Matthew A. Kupinski. "Design of a practical model-observer-based image quality assessment method for x-ray computed tomography imaging systems." SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS, 2016. http://hdl.handle.net/10150/622347.
Full textTaner, Serdar. "Image Classification For Content Based Indexing." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/2/1093269/index.pdf.
Full textTandan, Isabelle, and Erika Goteman. "Bank Customer Churn Prediction : A comparison between classification and evaluation methods." Thesis, Uppsala universitet, Statistiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-411918.
Full textMonari, Gaétan. "Sélection de modèles non linéaires par "leave-one-out": étude théorique et application des réseaux de neurones au procédé de soudage par points." Phd thesis, Université Pierre et Marie Curie - Paris VI, 1999. http://pastel.archives-ouvertes.fr/pastel-00000676.
Full textMONARI, GAETAN. "Selection de modeles non lineaires par leave-one-out ; etude theorique et application des reseaux de neurones au procede de soudage par points." Paris 6, 1999. http://www.theses.fr/1999PA066349.
Full textBabay, Dhouib Amel. "Contribution à la modélisation de l'influence des caractéristiques des fibres de coton et des paramètres du processus sur les propriétés des filés classiques et flammés." Valenciennes, 2004. http://ged.univ-valenciennes.fr/nuxeo/site/esupversions/81169dfa-b5b8-4f19-b771-cfef62fe792a.
Full textNowadays, the industry of the short fibre spinning mill needs performing tools allowing the modelling of process and the prediction of the yarn properties in order to control the level of quality of its products, to satisfy the customers and to increase their competitivity. Within this framework, the spinner, willing to improve and adapt continuously his product, must have more rational knowledge of the process of spinning mill, in particular concerning the correlations between the fibre and the yarn characteristics. Indeed, the fibre properties have a determining influence on the working of the machines, the quality and the cost of the final product. Thus, we propose a prediction system made up of several neuronal models allowing to predict the properties of two categories of yarns: ring spun and fancy yarns starting from the knowledge of the characteristics of cotton fibres and the structural parameters. The choice of the neuronal models having the optimal architecture was accomplished thanks to the application of an original approach called "virtual leave-one-out". The performances of the established models are evaluated and analyzed on the basis of a real data base gathered from an industrial spinning mill manufacturing yarns for the "denim" applications. Finally, the analysis of the impact of the input variables on the various model outputs has confirmed the validity of the variation of the modelled properties and has also shown that the established models reflect very well the experimental reality
Horn, Jean-François. "Diagnostic des maladies neurodégénératives à partor d'images obtenues par tomographie d'émission monophotonique et à l'aide de méthodes de classement avec apprentissage supervisé." Paris 6, 2009. http://www.theses.fr/2009PA066454.
Full textDizon, Lucas, and Martin Johansson. "Atrial Fibrillation Detection Algorithm Evaluation and Implementation in Java." Thesis, KTH, Skolan för teknik och hälsa (STH), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-158878.
Full textFörmaksflimmer är en vanlig hjärtrytmrubbning som kännetecknas av en avsaknad eller oregelbunden kontraktion av förmaken. Sjukdomen är en riskfaktor för andra allvarligare sjukdomar och de totala kostnaderna för samhället är betydande. Det skulle därför vara fördelaktigt att effektivisera och förbättra prevention samt diagnostisering av förmaksflimmer. Kliniskt diagnostiseras förmaksflimmer med hjälp av till exempel pulspalpation och auskultation av hjärtat, men diagnosen brukar fastställas med en EKG-undersökning. Det finns idag flertalet algoritmer för att detektera arytmin genom att analysera ett EKG. En av de vanligaste metoderna är att undersöka variabiliteten av hjärtrytmen (HRV) och utföra olika sorters statistiska beräkningar som kan upptäcka episoder av förmaksflimmer som avviker från en normal sinusrytm. I detta projekt har två metoder för att detektera förmaksflimmer utvärderats i Matlab, en baseras på beräkningar av variationskoefficienten och den andra använder sig av logistisk regression. EKG som kommer från databasen Physionet MIT används för att träna och testa modeller av algoritmerna. Innan EKG-signalen kan användas måste den behandlas för att ta bort olika typer av brus och artefakter. Vid test av algoritmen med variationskoefficienten blev resultatet en sensitivitet på 91,38%, en specificitet på 93,93% och en noggrannhet på 92,92%. För logistisk regression blev sensitiviteten 97,23%, specificiteten 93,79% och noggrannheten 95,39%. Algoritmen med logistisk regression presterade bättre och valdes därför för att implementeras i Java, där uppnåddes en sensitivitet på 91,31%, en specificitet på 93,47% och en noggrannhet på 95,25%.
Brites, Alice Dantas. "Monitoramento dos efeitos ecológicos e socioeconômicos da comercialização de produtos florestais não madereiros." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/90/90131/tde-24032011-215203/.
Full textAmazon, non-timber forest products, ecological effects, socioeconomic effects, monitoring.
Jhou, Wan-Jhen, and 周琬真. "Convolutive independent component analysis by density estimation and leave-one-out approximation." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/60627297990860247707.
Full text國立東華大學
應用數學系
93
This work explores blind separation of convolutive mixtures of independent sources. The convolutive structure consists of multiple, e.g. τ, mixing matrices, each corresponding to a different time delay, through which a segment of consecutive source signals are convoluted to form an observation. Based on the convolutive structure, the observations are temporally correlated among their different components. As τ = 1, the convolutive structure reduces to a linear transformation that produces temporally uncorrelated observations among different components, simply to a case typically attacked by independent component analysis (ICA). For arbitrary τ, estimating the convolutive structure as well as source signals subject to given observations decomposes to τ simultaneous sub-tasks following the leave-one approximation, each corresponding to optimizing a mixing matrix subject to a set of intermediate observations, each measuring the convolutive result of source signals through the other τ-1 mixing matrices. By the decomposition, each of τ simultaneous sub-tasks translates to a typical ICA task and can be directly resolved by the density estimation based ICA algorithm here. Numerical simulations show the novel approach is effective for blind source separation of fetal ECG and event-related potentials (ERP) signals.
Books on the topic "Leave one out method"
Busacca, Maurizio, and Roberto Paladini. Collaboration Age. Venice: Fondazione Università Ca’ Foscari, 2020. http://dx.doi.org/10.30687/978-88-6969-424-0.
Full textStrong, S. I. 3. Step one in the IRAC method: the issue. Oxford University Press, 2018. http://dx.doi.org/10.1093/he/9780198811152.003.0003.
Full textFoucault Welles, Brooke, and Sandra González-Bailón, eds. The Oxford Handbook of Networked Communication. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190460518.001.0001.
Full textTrout, J. D. All Talked Out. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190686802.001.0001.
Full textLafer, Gordon. The One Percent Solution. Cornell University Press, 2017. http://dx.doi.org/10.7591/cornell/9781501703065.001.0001.
Full textHoffman, Lawrence A. Jewish Liturgy and Jewish Scholarship: Method and Cosmology. Edited by Martin Goodman. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199280322.013.0029.
Full textRose, Jonathan. Readers' Liberation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198723554.001.0001.
Full textWilliam A, Schabas. Part 8 Appeal and Revision: Appel et Révision, Art.82 Appeal against other decisions/Appel d’autres décisions. Oxford University Press, 2016. http://dx.doi.org/10.1093/law/9780198739777.003.0087.
Full textBanati, Prerna, ed. Sustainable Human Development Across the Life Course. Policy Press, 2021. http://dx.doi.org/10.1332/policypress/9781529204827.001.0001.
Full textCaney, Simon. Justice and Posterity. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198813248.003.0009.
Full textBook chapters on the topic "Leave one out method"
Tsumoto, Shusaku, and Shoji Hirano. "Evaluation of Leave-One Out Method Based on Incremental Sampling Scheme." In Rough Sets and Intelligent Systems Paradigms, 225–36. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08729-0_22.
Full textChernousova, Elena, Nikolay Razin, Olga Krasotkina, Vadim Mottl, and David Windridge. "Linear Regression via Elastic Net: Non-enumerative Leave-One-Out Verification of Feature Selection." In Clusters, Orders, and Trees: Methods and Applications, 377–90. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0742-7_22.
Full textZhang, Tong. "A Leave-One-out Cross Validation Bound for Kernel Methods with Applications in Learning." In Lecture Notes in Computer Science, 427–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44581-1_28.
Full textWebb, Geoffrey I., Claude Sammut, Claudia Perlich, Tamás Horváth, Stefan Wrobel, Kevin B. Korb, William Stafford Noble, et al. "Leave-One-Out Error." In Encyclopedia of Machine Learning, 601. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_470.
Full textTsuda, Koji, and Motoaki Kawanabe. "The Leave-One-Out Kernel." In Artificial Neural Networks — ICANN 2002, 727–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46084-5_118.
Full textWebb, Geoffrey I., Claude Sammut, Claudia Perlich, Tamás Horváth, Stefan Wrobel, Kevin B. Korb, William Stafford Noble, et al. "Leave-One-Out Cross-Validation." In Encyclopedia of Machine Learning, 600–601. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_469.
Full textAckermann, Friedrich, Grit Herrmann, Stefan Posch, and Gerhard Sagerer. "Evaluierung eines Protein-Dockingsystems durch Leave-One-Out-Test." In Informatik aktuell, 130–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-80294-2_14.
Full textRuxin, Qin, Chen Jing, Deng Naiyang, and Tian Yingjie. "A Leave-One-Out Bound for ν−Support Vector Regression." In Computational Science – ICCS 2007, 669–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72588-6_113.
Full textYuan, Jin, Yan-Ming Li, Cheng-Liang Liu, and Xuan F. Zha. "Leave-One-Out Cross-Validation Based Model Selection for Manifold Regularization." In Advances in Neural Networks - ISNN 2010, 457–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13278-0_59.
Full textBo, Liefeng, Ling Wang, and Licheng Jiao. "Multiple Parameter Selection for LS-SVM Using Smooth Leave-One-Out Error." In Advances in Neural Networks — ISNN 2005, 851–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11427391_136.
Full textConference papers on the topic "Leave one out method"
Wang, Xiaolin, Masao Utiyama, Andrew Finch, Taro Watanabe, and Eiichiro Sumita. "Leave-one-out Word Alignment without Garbage Collector Effects." In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2015. http://dx.doi.org/10.18653/v1/d15-1209.
Full textTsumoto, Shusaku, and Shoji Hirano. "Formal analysis of leave-one out method based on decremental sampling scheme." In 2014 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2014. http://dx.doi.org/10.1109/smc.2014.6974349.
Full textLiu, Jianguo, Neil Danait, Shawn Hu, and Sayon Sengupta. "A leave-one-feature-out wrapper method for feature selection in data classification." In 2013 6th International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2013. http://dx.doi.org/10.1109/bmei.2013.6747021.
Full textFukuta, Kentaro, Tomomasa Nagashima, and Yoshifumi Okada. "LEAF: Leave-One-Out Forward Selection Method for Cancer Classification Using Gene Expression Data." In 2010 IEEE/ACIS 9th International Conference on Computer and Information Science (ICIS). IEEE, 2010. http://dx.doi.org/10.1109/icis.2010.132.
Full textTsumoto, Shusaku, and Shoji Hirano. "Formal Analysis of Leave-One-Out Methods Based on Decremental Sampling Scheme." In 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT). IEEE, 2014. http://dx.doi.org/10.1109/wi-iat.2014.121.
Full textZhang, Bo, Xianyuan Huang, Long Fan, and Guobin Chang. "Sparseness of the LS-SVM algorithm based on the leave one out cross validation method with Multibeam data." In EITCE 2020: 2020 4th International Conference on Electronic Information Technology and Computer Engineering. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3443467.3443763.
Full textWang, Yu-Jen, Shyang-Jye Chang, Kuo-Chieh Fu, and Chien-Erh Weng. "Design of a Dual-Resonance Excitation Langevin Piezoelectric Actuator Using Taguchi Method." In ASME 2016 Conference on Information Storage and Processing Systems. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/isps2016-9506.
Full textZurru, Antioco Luigi, Antonello Mura, and Ilaria Tatulli. "Leave no one behind. Design inclusive motor activities in Primary Teacher Education Courses." In Fifth International Conference on Higher Education Advances. Valencia: Universitat Politècnica València, 2019. http://dx.doi.org/10.4995/head19.2019.9411.
Full textBlack, Emily, and Matt Fredrikson. "Leave-one-out Unfairness." In FAccT '21: 2021 ACM Conference on Fairness, Accountability, and Transparency. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3442188.3445894.
Full textBender, Dieter, Ali Jalali, Daniel J. Licht, and C. Nataraj. "Prediction of Periventricular Leukomalacia Occurrence in Neonates Using a Novel Support Vector Machine Classifier Optimization Method." In ASME 2015 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/dscc2015-9984.
Full textReports on the topic "Leave one out method"
Iyer, R., J. P. Shulka, and A. Verma. Community Leave No One Behind: Lessons from a Pilot. Institute of Development Studies (IDS), July 2021. http://dx.doi.org/10.19088/slh.2021.014.
Full textMARTINEZ, RUBEL F. A Visual Empirical Region of Influence Pattern Recognition Tool for Leave-One-Out Data Analysis. Office of Scientific and Technical Information (OSTI), March 2002. http://dx.doi.org/10.2172/793409.
Full textShukla, J. P., and Anupma Verma. Community Leave No One Behind: Handbook For Practitioners. Institute of Development Studies (IDS), July 2021. http://dx.doi.org/10.19088/slh.2021.015.
Full textLi, Howell, Tom Platte, Jijo K. Mathew, W. Benjamin Smith, Enrique Saldivar-Carranza, and Darcy M. Bullock. Using Connected Vehicle Data to Reassess Dilemma Zone Performance of Heavy Vehicles. Purdue University, 2020. http://dx.doi.org/10.5703/1288284317321.
Full textDowning, W. Logan, Howell Li, William T. Morgan, Cassandra McKee, and Darcy M. Bullock. Using Probe Data Analytics for Assessing Freeway Speed Reductions during Rain Events. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317350.
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