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: 9 de junio 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 (August 24, 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, as the presence of outliers in the data set can cause the assumptions of linear discriminant analysis to be violated and result in inaccurate classification outcomes. Therefore, it is necessary to handle outliers using robust methods in discriminant analysis. One such robust method in discriminant analysis is the Minimum Covariance Determinant (MCD), which is highly effective in dealing with outliers and relatively easier to apply compared to other robust methods. The aim of this study is to compare the classification results of linear discriminant analysis with robust linear discriminant analysis on the dataset of diabetes patients at RSUD Padangsidimpuan in 2023. The results obtained from this dataset indicate that linear discriminant analysis achieved an accuracy of 85,71%, while robust linear discriminant analysis achieved an accuracy of 80,95%. These findings suggest that the use of linear discriminant analysis and robust linear discriminant analysis can yield different results depending on the characteristics of the data and the number of outliers in the dataset.
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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 (April 1, 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 (June 26, 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 (March 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 (March 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 (January 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 (December 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 missing data leads to a progressive decline in classification accuracy. With regard to the effect of additional data on classification robustness the results show a less predictable effect which can only be characterised as a general tendency towards improved robustness.
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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 units receiving constant input and time fluctuating noise whose correlation across units can be tuned independently of firing rate. We implement a perceptron-like readout with Fisher Linear Discriminant Analysis (LDA). We exploit its closed form solution to find explicit expressions for discrimination error as a function of network parameters (leak, shared inputs, and noise gain) as well as the strength of noise correlation. First, we derive equations for discrimination error as a function of noise correlation. We find that four qualitatively different sets of results exist, based on the ratios of the difference of means and variance of the distributions of neural activity. From network parameters, we find the conditions for which an increase in noise correlation can lead to monotonic decrease or monotonic increase of error, as well as conditions for which error evolves non-monotonically as a function of correlations. These results provide a potential explanation for previously reported contradictory effects of noise correlation. Second, we expand on the dependency of the quantitative behaviour of the error curve on the tuning of specific subsets of network parameters. Particularly, when the noise gain of a pair of units is increased, the error rate as a function of noise correlation increases multiplicatively. However, when the noise gain of a single unit is increased, under certain conditions, the effect of noise can be beneficial to stimulus discrimination. In sum, we present a framework of analysis that explains a series of non-trivial properties of neuronal discrimination via a simple linear classifier. We show explicitly how different configurations of parameters can lead to drastically different conclusions on the impact of noise correlations. These effects shed light on abundant experimental and computational results reporting conflicting effects of noise correlations. The derived analyses rely on few assumptions and may therefore be applicable to a broad class of neural models whose activity can be approximated by a multivariate distribution.
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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. Salford: 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. London: 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. London: Controller, HMSO, 1991.

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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 of life. This process can be cast in a probabilistic framework and used to better understand the mechanisms behind habitat preferences and selection. There are at least six distinctly different statistical approaches to conducting a habitat analysis – that is, identifying and quantifying the environmental variables that a species most strongly associates with. These are (1) comparison among group means (e.g., ANOVA), (2) multiple linear regression, (3) multiple logistic regression, (4) classification and regression trees, (5) multivariate techniques (Principal Components Analysis and Discriminant Function Analysis), and (6) occupancy modelling. Each of these is lucidly explained and demonstrated by application to a hypothetical dataset. The strengths and weaknesses of each method are discussed. Given the ongoing biodiversity crisis largely caused by habitat destruction, there is a crucial and general need to better characterize and understand the habitat requirements of many different species, particularly those that are threatened and endangered.
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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 kernel rules. It also describes classification based on partial least squares, classification based on reproducing kernels, and depth-based classification. Finally, it examines unsupervised classification methods, focusing on K-means for functional data, K-means for data in a Hilbert space, and impartial trimmed K-means for functional data. Some practical issues, in particular real-data examples and simulations, are reviewed and some selected proofs are given.
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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, 49–66. Berkeley, CA: 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, 71–85. Cham: 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, 237–80. New York, NY: 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, 1132–33. New York, NY: 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, 27–33. New York, NY: 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, 53–70. Berkeley, CA: 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, 207–28. Boca Raton: 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, 343–50. Berlin, Heidelberg: 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, 133–40. Berlin, Heidelberg: 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, 531–42. Berlin, Heidelberg: 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. New York, New York, USA: 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, August 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 conduct numerical simulations to compare misclassification probabilities using true and estimated mean parameters, as well as probabilities computed through simulation. Our findings suggest that as the distance between groups widens, the misclassification probability curve decreases, indicating that classifying observations is easier in widely separated groups compared to closely clustered ones.
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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.), December 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 the non-cerebral sensors that contribute most significantly to separability of data acquired during seizures from non-seizure data. Post-processing of the data using linear discriminant analysis (LDA) shows that seizure data are strongly separable from non-seizure data based on features derived from the signals recorded. The mean area under the receiver operator characteristic (ROC) curve for each individual patient that experienced a seizure during data collection, calculated using LDA, was 0.9682. The features that contribute most significantly to seizure detection differ for each patient. The results show that a multimodal approach to seizure detection using the specified sensor suite is promising in detecting seizures with both sensitivity and specificity. Moreover, the study provides a means to quantify the contribution of each sensor and feature to separability. Development of a non-electroencephalography (EEG) based seizure detection device would give doctors a more accurate seizure count outside of the clinical setting, improving treatment and the quality of life of epilepsy patients.
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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, November 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 time assessment of nitrogen status of corn using a mobile sensor with the potential to regulate nitrogen application based on data from that sensor. Specifically, the research first attempted to determine the system parameters necessary to optimize reflectance spectra of corn plants as a function of growth stage, chlorophyll and nitrogen status. In addition to that, an adaptable, multispectral sensor and the signal processing algorithm to provide real time, in-field assessment of corn nitrogen status was developed. Spectral characteristics of corn leaves reflectance were investigated in order to estimate the nitrogen status of the plants, using a commercial laboratory spectrometer. Statistical models relating leaf N and reflectance spectra were developed for both greenhouse and field plots. A basis was established for assessing nitrogen status using spectral reflectance from plant canopies. The combined effect of variety and N treatment was studied by measuring the reflectance of three varieties of different leaf characteristic color and five different N treatments. The variety effect on the reflectance at 552 nm was not significant (a = 0.01), while canonical discriminant analysis showed promising results for distinguishing different variety and N treatment, using spectral reflectance. Ambient illumination was found inappropriate for reliable, one-beam spectral reflectance measurement of the plants canopy due to the strong spectral lines of sunlight. Therefore, artificial light was consequently used. For in-field N status measurement, a dark chamber was constructed, to include the sensor, along with artificial illumination. Two different approaches were tested (i) use of spatially scattered artificial light, and (ii) use of collimated artificial light beam. It was found that the collimated beam along with a proper design of the sensor-beam geometry yielded the best results in terms of reducing the noise due to variable background, and maintaining the same distance from the sensor to the sample point of the canopy. A multispectral sensor assembly, based on a linear variable filter was designed, constructed and tested. The sensor assembly combined two sensors to cover the range of 400 to 1100 nm, a mounting frame, and a field data acquisition system. Using the mobile dark chamber and the developed sensor, as well as an off-the-shelf sensor, in- field nitrogen status of the plants canopy was measured. Statistical analysis of the acquired in-field data showed that the nitrogen status of the com leaves can be predicted with a SEP (Standard Error of Prediction) of 0.27%. The stage of maturity of the crop affected the relationship between the reflectance spectrum and the nitrogen status of the leaves. Specifically, the best prediction results were obtained when a separate model was used for each maturity stage. In-field assessment of the nitrogen status of corn leaves was successfully carried out by non contact measurement of the reflectance spectrum. This technology is now mature to be incorporated in field implements for on-line control of fertilizer application.
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Searcy, Stephen W., and Kalman Peleg. Adaptive Sorting of Fresh Produce. United States Department of Agriculture, August 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 wavelength imaging sensor was constructed and tested using the selected technology. The sensor was tested and algorithms for multispectral image acquisition were developed. A high speed inspection system for fresh-market carrots was built and tested. It was shown that a combination of efficient parallel processing of a DSP and a PC based host CPU in conjunction with a hierarchical classification system, yielded an inspection system capable of handling 2 carrots per second with a classification accuracy of more than 90%. The adaptive sorting technique was extensively investigated and conclusively demonstrated to reduce misclassification rates in comparison to conventional non-adaptive sorting. The adaptive classifier algorithm was modeled and reduced to a series of modules that can be added to any existing produce sorting machine. A simulation of the entire process was created in Matlab using a graphical user interface technique to promote the accessibility of the difficult theoretical subjects. Typical Grade classifiers based on k-Nearest Neighbor techniques and linear discriminants were implemented. The sample histogram, estimating the cumulative distribution function (CDF), was chosen as a characterizing feature of prototype populations, whereby the Kolmogorov-Smirnov statistic was employed as a population classifier. Simulations were run on artificial data with two-dimensions, four populations and three classes. A quantitative analysis of the adaptive classifier's dependence on population separation, training set size, and stack length determined optimal values for the different parameters involved. The technique was also applied to a real produce sorting problem, e.g. an automatic machine for sorting dates by machine vision in an Israeli date packinghouse. Extensive simulations were run on actual sorting data of dates collected over a 4 month period. In all cases, the results showed a clear reduction in classification error by using the adaptive technique versus non-adaptive sorting.
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