Bibliografías temáticas / Linear discriminant analysis

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Literatura académica sobre el tema "Linear discriminant analysis"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 25 de julio de 2025

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Artículos de revistas sobre el tema "Linear discriminant analysis"

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Hino, Hideitsu, and Jun Fujiki. "ADHERENTLY PENALIZED LINEAR DISCRIMINANT ANALYSIS." Journal of the Japanese Society of Computational Statistics 28, no. 1 (2015): 125–37. http://dx.doi.org/10.5183/jjscs.1412001_219.

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Fitri, Fitri Hayati, Dodi Vionanda, Yenni Kurniawati, and Tessy Octavia Mukhti. "Comparison of Linear Discriminant Analysis with Robust Linear Discriminant Analysis." UNP Journal of Statistics and Data Science 2, no. 3 (2024): 353–59. http://dx.doi.org/10.24036/ujsds/vol2-iss3/206.

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Discriminant analysis is a multivariate technique related to separating distinct groups of objects and allocating new objects to predefined groups. Discriminant analysis produces a discriminant function, which is defined as a linear combination of independent variables used to classify objects into two or more groups or categories. Assumptions that must be met in linear discriminant analysis include the independent variables being multivariate normally distributed and the covariance matrices for each group being equal. It is also necessary to identify outliers in linear discriminant analysis,
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Zhu, Fa, Junbin Gao, Jian Yang, and Ning Ye. "Neighborhood linear discriminant analysis." Pattern Recognition 123 (March 2022): 108422. http://dx.doi.org/10.1016/j.patcog.2021.108422.

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Yahaya, Sharipah Soaad Syed, Yai-Fung Lim, Hazlina Ali, and Zurni Omar. "Robust Linear Discriminant Analysis." Journal of Mathematics and Statistics 12, no. 4 (2016): 312–16. http://dx.doi.org/10.3844/jmssp.2016.312.316.

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Hu, Wei, Weining Shen, Hua Zhou, and Dehan Kong. "Matrix Linear Discriminant Analysis." Technometrics 62, no. 2 (2019): 196–205. http://dx.doi.org/10.1080/00401706.2019.1610069.

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Na, Jin Hee, Myoung Soo Park, and Jin Young Choi. "Linear boundary discriminant analysis." Pattern Recognition 43, no. 3 (2010): 929–36. http://dx.doi.org/10.1016/j.patcog.2009.09.015.

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Chen, Songcan, and Daohong Li. "Modified linear discriminant analysis." Pattern Recognition 38, no. 3 (2005): 441–43. http://dx.doi.org/10.1016/j.patcog.2004.08.008.

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Tang, Hong, Tao Fang, and Peng-Fei Shi. "Laplacian linear discriminant analysis." Pattern Recognition 39, no. 1 (2006): 136–39. http://dx.doi.org/10.1016/j.patcog.2005.06.016.

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Zhao, Jianhua, Philip L. H. Yu, Lei Shi, and Shulan Li. "Separable linear discriminant analysis." Computational Statistics & Data Analysis 56, no. 12 (2012): 4290–300. http://dx.doi.org/10.1016/j.csda.2012.04.003.

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Cai, Wei, Guoyu Guan, Rui Pan, Xuening Zhu, and Hansheng Wang. "Network linear discriminant analysis." Computational Statistics & Data Analysis 117 (January 2018): 32–44. http://dx.doi.org/10.1016/j.csda.2017.07.007.

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Tesis sobre el tema "Linear discriminant analysis"

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Skinner, Evelina. "Linear Discriminant Analysis with Repeated Measurements." Thesis, Linköpings universitet, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-162777.

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The classification of observations based on repeated measurements performed on the same subject over a given period of time or under different conditions is a common procedure in many disciplines such as medicine, psychology and environmental studies. In this thesis repeated measurements follow the Growth Curve model and are classified using linear discriminant analysis. The aim of this thesis is both to examine the effect of missing data on classification accuracy and to examine the effect of additional data on classification robustness. The results indicate that an increasing amount of missi
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Ganeshanandam, S. "Variable selection in two-group discriminant analysis using the linear discriminant function." Thesis, University of Reading, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.379265.

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Kim, Jiae. "Nonlinear Generalizations of Linear Discriminant Analysis: the Geometry of the Common Variance Space and Kernel Discriminant Analysis." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1607019187556971.

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Li, Yongping. "Linear discriminant analysis and its application to face identification." Thesis, University of Surrey, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326513.

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Calderini, Matias. "Linear Discriminant Analysis and Noise Correlations in Neuronal Activity." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39962.

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The effects of noise correlations on neuronal stimulus discrimination have been the subject of sustained debate. Both experimental and computational work suggest beneficial and detrimental contributions of noise correlations. The aim of this study is to develop an analytically tractable model of stimulus discrimination that reveals the conditions leading to improved or impaired performance from model parameters and levels of noise correlation. We begin with a mean firing rate integrator model as an approximation of underlying spiking activity in neuronal circuits. We consider two independent
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Nguyen, Hoang-Huy [Verfasser]. "Multi-Step Linear Discriminant Analysis and Its Applications / Hoang Huy Nguyen." Greifswald : Universitätsbibliothek Greifswald, 2013. http://d-nb.info/1030246793/34.

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Van, Deventer Petrus Jacobus Uys. "Outliers, influential observations and robust estimation in non-linear regression analysis and discriminant analysis." Doctoral thesis, University of Cape Town, 1993. http://hdl.handle.net/11427/4363.

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Draper, John Daniel. "Simultaneous Adaptive Fractional Discriminant Analysis: Applications to the Face Recognition Problem." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1331096665.

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NAKAGAWA, Seiichi, Norihide KITAOKA, and Makoto SAKAI. "Linear Discriminant Analysis Using a Generalized Mean of Class Covariances and Its Application to Speech Recognition." Institute of Electronics, Information and Communication Engineers, 2008. http://hdl.handle.net/2237/14967.

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Hennon, Christopher C. "Investigating Probabilistic Forecasting of Tropical Cyclogenesis Over the North Atlantic Using Linear and Non-Linear Classifiers." The Ohio State University, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=osu1047237423.

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Libros sobre el tema "Linear discriminant analysis"

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Vadera, S. Learning after trees by incorporating linear discriminant analysis. University of Salford Department of Mathematics and Computer Science, 1995.

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Peeling, S. M. The use of linear discriminant analysis in the ARM continuous speech recognition system. Controller H.M.S.O., 1992.

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Peeling, S. M. Preliminiary results on the use of linear discriminant analysis in the ARM continuous speech recognition system. Controller, HMSO, 1991.

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4

Derek, A. GENERALIZED LINEAR MODELS. POISSON REGRESSION, LOGISTIC REGRESSION, DECISION TREES and DISCRIMINANT ANALYSIS. Lulu Press, Inc., 2020.

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Veech, Joseph A. Habitat Ecology and Analysis. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198829287.001.0001.

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Habitat is crucial to the survival and reproduction of individual organisms as well as persistence of populations. As such, species-habitat relationships have long been studied, particularly in the field of wildlife ecology and to a lesser extent in the more encompassing discipline of ecology. The habitat requirements of a species largely determine its spatial distribution and abundance in nature. One way to recognize and appreciate the over-riding importance of habitat is to consider that a young organism must find and settle into the appropriate type of habitat as one of the first challenges
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Sharif, Shamshuritawati, Hazlina Ali, and Sharipah Soaad Syed Yahaya. Multivariate statistic for researchers. UUM Press, 2016. http://dx.doi.org/10.32890/9789670876764.

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This book is a valuable resource for those engaged in multivariate statistical techniques. Most chapters include a set of problems and solution that enable readers to overcome the drawback of the classical techniques.It covers a theoretical disadvantage of correlation and covariance test, Hotellings T2 statistic, local influence, and linear discriminant analysis to inspire new or young researchers with new ideas for theoretical improvement.This book is also worthy for people who want to learn multivariate statistics extensively.
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Baillo, Amparo, Antonio Cuevas, and Ricardo Fraiman. Classification methods for functional data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.10.

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This article reviews the literature concerning supervised and unsupervised classification of functional data. It first explains the meaning of unsupervised classification vs. supervised classification before discussing the supervised classification problem in the infinite-dimensional case, showing that its formal statement generally coincides with that of discriminant analysis in the classical multivariate case. It then considers the optimal classifier and plug-in rules, empirical risk and empirical minimization rules, linear discrimination rules, the k nearest neighbor (k-NN) method, and kern
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Capítulos de libros sobre el tema "Linear discriminant analysis"

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Zhou, Hong. "Linear Discriminant Analysis." In Learn Data Mining Through Excel. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5982-5_4.

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Zhao, Haitao, Zhihui Lai, Henry Leung, and Xianyi Zhang. "Linear Discriminant Analysis." In Information Fusion and Data Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40794-0_5.

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Izenman, Alan Julian. "Linear Discriminant Analysis." In Springer Texts in Statistics. Springer New York, 2013. http://dx.doi.org/10.1007/978-0-387-78189-1_8.

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Liu, Zhi-Ping. "Linear Discriminant Analysis." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_395.

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Xanthopoulos, Petros, Panos M. Pardalos, and Theodore B. Trafalis. "Linear Discriminant Analysis." In SpringerBriefs in Optimization. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4419-9878-1_4.

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Zhou, Hong. "Linear Discriminant Analysis." In Learn Data Mining Through Excel. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9771-1_4.

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Cook, R. Dennis, and Liliana Forzani. "Linear Discriminant Analysis." In Partial Least Squares Regression. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003482475-7.

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Koch, Karl-Rudolf. "Discriminant Analysis." In Parameter Estimation and Hypothesis Testing in Linear Models. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-662-02544-4_6.

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Czogiel, Irina, Karsten Luebke, Marc Zentgraf, and Claus Weihs. "Localized Linear Discriminant Analysis." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-70981-7_16.

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Ioffe, Sergey. "Probabilistic Linear Discriminant Analysis." In Computer Vision – ECCV 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11744085_41.

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Actas de conferencias sobre el tema "Linear discriminant analysis"

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Makoto Sakai, Norihide Kitaoka, and Seiichi Nakagawa. "Power linear discriminant analysis." In 2007 9th International Symposium on Signal Processing and Its Applications (ISSPA). IEEE, 2007. http://dx.doi.org/10.1109/isspa.2007.4555418.

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Valcarcel Macua, Sergio, Pavle Belanovic, and Santiago Zazo. "Distributed linear discriminant analysis." In 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5946724.

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Xie, Shuilian, Mahdi Imani, Edward R. Dougherty, and Ulisses M. Braga-Neto. "Nonstationary linear discriminant analysis." In 2017 51st Asilomar Conference on Signals, Systems, and Computers. IEEE, 2017. http://dx.doi.org/10.1109/acssc.2017.8335158.

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Loog, M. "Conditional Linear Discriminant Analysis." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.402.

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Tianwei Xu, Chong Lu, and Wanquan Liu. "The matrix form for weighted linear discriminant analysis and fractional linear discriminant analysis." In 2009 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2009. http://dx.doi.org/10.1109/icmlc.2009.5212309.

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Pei, Yan. "Linear Principal Component Discriminant Analysis." In 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2015. http://dx.doi.org/10.1109/smc.2015.368.

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Lim, Yai-Fung, Sharipah Soaad Syed Yahaya, and Hazlina Ali. "Winsorization on linear discriminant analysis." In THE 4TH INTERNATIONAL CONFERENCE ON QUANTITATIVE SCIENCES AND ITS APPLICATIONS (ICOQSIA 2016). Author(s), 2016. http://dx.doi.org/10.1063/1.4966100.

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Siddiqui, Hasib, and Hau Hwang. "Sparse Fisher's linear discriminant analysis." In IS&T/SPIE Electronic Imaging, edited by Charles A. Bouman, Ilya Pollak, and Patrick J. Wolfe. SPIE, 2011. http://dx.doi.org/10.1117/12.887693.

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Moschoglou, Stylianos, Mihalis Nicolaou, Yannis Panagakis, and Stefanos Zafeiriou. "Initializing probabilistic linear discriminant analysis." In 2017 25th European Signal Processing Conference (EUSIPCO). IEEE, 2017. http://dx.doi.org/10.23919/eusipco.2017.8081393.

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Durrant, Robert J., and Ata Kaban. "Compressed fisher linear discriminant analysis." In the 16th ACM SIGKDD international conference. ACM Press, 2010. http://dx.doi.org/10.1145/1835804.1835945.

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Informes sobre el tema "Linear discriminant analysis"

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de Dieu Niyigena, Jean, Innocent Ngaruye, Joseph Nzabanita, and Martin Singull. Approximation of misclassification probabilities using quadratic classifier for repeated measurements with known covariance matrices. Linköping University Electronic Press, 2024. http://dx.doi.org/10.3384/lith-mat-r-2024-02.

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Quadratic discriminant analysis is a well-established supervised classification method, which extends the linear the linear discriminant analysis by relaxing the assumption of equal variances across classes. In this study, quadratic discriminant analysis is used to develop a quadratic classification rule based on repeated measurements. We employ a bilinear regression model to assign new observations to predefined populations and approximate the misclassification probability. Through weighted estimators, we estimate unknown mean parameters and derive moments of the quadratic classifier. We then
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Hamlin, Alexandra, Erik Kobylarz, James Lever, Susan Taylor, and Laura Ray. Assessing the feasibility of detecting epileptic seizures using non-cerebral sensor. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42562.

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This paper investigates the feasibility of using non-cerebral, time-series data to detect epileptic seizures. Data were recorded from fifteen patients (7 male, 5 female, 3 not noted, mean age 36.17 yrs), five of whom had a total of seven seizures. Patients were monitored in an inpatient setting using standard video electroencephalography (vEEG), while also wearing sensors monitoring electrocardiography, electrodermal activity, electromyography, accelerometry, and audio signals (vocalizations). A systematic and detailed study was conducted to identify the sensors and the features derived from t
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Alchanatis, Victor, Stephen W. Searcy, Moshe Meron, W. Lee, G. Y. Li, and A. Ben Porath. Prediction of Nitrogen Stress Using Reflectance Techniques. United States Department of Agriculture, 2001. http://dx.doi.org/10.32747/2001.7580664.bard.

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Commercial agriculture has come under increasing pressure to reduce nitrogen fertilizer inputs in order to minimize potential nonpoint source pollution of ground and surface waters. This has resulted in increased interest in site specific fertilizer management. One way to solve pollution problems would be to determine crop nutrient needs in real time, using remote detection, and regulating fertilizer dispensed by an applicator. By detecting actual plant needs, only the additional nitrogen necessary to optimize production would be supplied. This research aimed to develop techniques for real tim
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Searcy, Stephen W., and Kalman Peleg. Adaptive Sorting of Fresh Produce. United States Department of Agriculture, 1993. http://dx.doi.org/10.32747/1993.7568747.bard.

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This project includes two main parts: Development of a “Selective Wavelength Imaging Sensor” and an “Adaptive Classifiery System” for adaptive imaging and sorting of agricultural products respectively. Three different technologies were investigated for building a selectable wavelength imaging sensor: diffraction gratings, tunable filters and linear variable filters. Each technology was analyzed and evaluated as the basis for implementing the adaptive sensor. Acousto optic tunable filters were found to be most suitable for the selective wavelength imaging sensor. Consequently, a selectable wave
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