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Relevant bibliographies by topics / Weight matrices

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Academic literature on the topic 'Weight matrices'

Author: Grafiati

Published: 5 June 2025

Last updated: 24 June 2025

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Journal articles on the topic "Weight matrices"

1

Gross, Marco. "Estimating GVAR weight matrices." Spatial Economic Analysis 14, no. 2 (2018): 219–40. http://dx.doi.org/10.1080/17421772.2019.1556800.

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Pastur, L. "Eigenvalue distribution of large random matrices arising in deep neural networks: Orthogonal case." Journal of Mathematical Physics 63, no. 6 (2022): 063505. http://dx.doi.org/10.1063/5.0085204.

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This paper deals with the distribution of singular values of the input–output Jacobian of deep untrained neural networks in the limit of their infinite width. The Jacobian is the product of random matrices where the independent weight matrices alternate with diagonal matrices whose entries depend on the corresponding column of the nearest neighbor weight matrix. The problem has been considered in the several recent studies of the field for the Gaussian weights and biases and also for the weights that are Haar distributed orthogonal matrices and Gaussian biases. Based on a free probability argument, it was claimed in those papers that, in the limit of infinite width (matrix size), the singular value distribution of the Jacobian coincides with that of the analog of the Jacobian with special random but weight independent diagonal matrices, the case well known in random matrix theory. In this paper, we justify the claim for random Haar distributed weight matrices and Gaussian biases. This, in particular, justifies the validity of the mean field approximation in the infinite width limit for the deep untrained neural networks and extends the macroscopic universality of random matrix theory to this new class of random matrices.

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Alphonce, Christian B., and Cathal M. Brugha. "Extracting Consistent Weight Ratio Matrices from Inconsistent Judgment Matrices." Tanzania Journal of Engineering and Technology 22, no. 2 (1998): 247–58. http://dx.doi.org/10.52339/tjet.v22i2.282.

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Kyazhin, S. N. "Weight properties of primitive matrices." Prikladnaya diskretnaya matematika. Prilozhenie, no. 11 (September 1, 2018): 10–12. http://dx.doi.org/10.17223/2226308x/11/2.

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Giraud, Mathieu, and Jean-Stéphane Varré. "Parallel Position Weight Matrices algorithms." Parallel Computing 37, no. 8 (2011): 466–78. http://dx.doi.org/10.1016/j.parco.2010.10.001.

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Ermagun, Alireza, and David M. Levinson. "Development and application of the network weight matrix to predict traffic flow for congested and uncongested conditions." Environment and Planning B: Urban Analytics and City Science 46, no. 9 (2018): 1684–705. http://dx.doi.org/10.1177/2399808318763368.

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To capture network dependence between traffic links, we introduce two distinct network weight matrices ([Formula: see text]), which replace spatial weight matrices used in traffic forecasting methods. The first stands on the notion of betweenness centrality and link vulnerability in traffic networks. To derive this matrix, we use an unweighted betweenness method and assume all traffic flow is assigned to the shortest path. The other relies on flow rate change in traffic links. For forming this matrix, we use the flow information of traffic links and employ user equilibrium assignment and the method of successive averages algorithm to solve the network. The components of the network weight matrices are a function not simply of adjacency, but of network topology, network structure, and demand configuration. We test and compare the network weight matrices in different traffic conditions using the Nguyen–Dupuis network. The results lead to a conclusion that the network weight matrices operate better than traditional spatial weight matrices. Comparing the unweighted and flow-weighted network weight matrices, we also reveal that the assigned flow network weight matrices perform two times better than a betweenness network weight matrix, particularly in congested traffic conditions.

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Truong, S. N. "A Ternary Neural Network with Compressed Quantized Weight Matrix for Low Power Embedded Systems." Engineering, Technology & Applied Science Research 12, no. 2 (2022): 8311–15. http://dx.doi.org/10.48084/etasr.4758.

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In this paper, we propose a method of transforming a real-valued matrix to a ternary matrix with controllable sparsity. The sparsity of quantized weight matrices can be controlled by adjusting the threshold during the training and quantizing process. A 3-layer ternary neural network was trained with the MNIST dataset using the proposed adjustable dynamic threshold. The sparsity of the quantized weight matrices varied from 0.1 to 0.6 and the obtained recognition rate reduced from 91% to 88%. The sparse weight matrices were compressed by the compressed sparse row format to speed up the ternary neural network, which can be deployed on low-power embedded systems, such as the Raspberry Pi 3 board. The ternary neural network with the sparsity of quantized weight matrices of 0.1 is 4.24 times faster than the ternary neural network without compressing weight matrices. The ternary neural network is faster as the sparsity of quantized weight matrices increases. When the sparsity of the quantized weight matrices is as high as 0.6, the recognition rate degrades by 3%, however, the speed is 9.35 times the ternary neural network's without compressing quantized weight matrices. Ternary neural network work with compressed sparse matrices is feasible for low-cost, low-power embedded systems.

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Arasu, K. T., Ka Hin Leung, Siu Lun Ma, Ali Nabavi, and D. K. Ray-Chaudhuri. "Circulant weighing matrices of weight 22t." Designs, Codes and Cryptography 41, no. 1 (2006): 111–23. http://dx.doi.org/10.1007/s10623-006-0026-2.

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Teofanov, Nenad, and Filip Tomić. "Extended Gevrey Regularity via Weight Matrices." Axioms 11, no. 10 (2022): 576. http://dx.doi.org/10.3390/axioms11100576.

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The main aim of this paper is to compare two recent approaches for investigating the interspace between the union of Gevrey spaces Gt(U) and the space of smooth functions C∞(U). The first approach in the style of Komatsu is based on the properties of two parameter sequences Mp=pτpσ, τ>0, σ>1. The other one uses weight matrices defined by certain weight functions. We prove the equivalence of the corresponding spaces in the Beurling case by taking projective limits with respect to matrix parameters, while in the Roumieu case we need to consider a larger space than the one obtained as the inductive limit of extended Gevrey classes.

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Arasu, K. T., and Dina Torban. "New weighing matrices of weight 25." Journal of Combinatorial Designs 7, no. 1 (1999): 11–15. http://dx.doi.org/10.1002/(sici)1520-6610(1999)7:1<11::aid-jcd2>3.0.co;2-4.

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Dissertations / Theses on the topic "Weight matrices"

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Nabavi, Ali. "The spectrum of circulant weighing matrices of weight 16 /." The Ohio State University, 2000. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488203552780954.

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Ozturk, Ufuk. "Interval Priority Weight Generation From Interval Comparison Matrices In Analytic Hierarchy Process." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12611031/index.pdf.

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In this study, for the well-known Analytic Hierarchy Process (AHP) method a new approach to interval priority weight generation from interval comparison matrix is proposed. This method can be used for both inconsistent and consistent matrices. Also for the problems having more than two hierarchical levels a synthesizing heuristic is presented. The performances of the methods, interval generation and synthesizing, are compared with the methods that are already available in the literature on randomly generated matrices.

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Višňovský, Marek. "Prediction and Analysis of Nucleosome Positions in DNA." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-412874.

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Eukaryotní DNA se váže kolem nukleozomů, čím ovplyvnuje vyšši strukturu DNA a přístup k vazebním mistům pro všeobecní transkripční faktory a oblasti genů. Je proto důležité vědet, kde se nukleozomy vážou na DNA, a jak silná tato vazba je, abychom mohli porozumět mechanizmům regulace genů. V rámci projektu byla implementována nová metoda pro predikci nukleozomů založená na rozšíření Skrytých Markovových modelů, kde jako trénovací a testovací sada posloužila publikována data z Brogaard et al. (Brogaard K, Wang J-P, Widom, J. Nature 486(7404), 496-501 (2012). doi:10.1038/nature11142). Správne predikováno bylo zhruba 50% nukleozomů, co je porovnatenlný výsledek s existujícimi metodami. Okrem toho byla provedena řada experimentů popisující vlastnosti sekvencí nukleozomů a ich organizace.

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Shi, Qiling. "Weighted lp-Stability for Localized Infinite Matrices." Doctoral diss., University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4028.

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This dissertation originates from a classical result that the lp-stability of the convolution operator associated with a summable sequence are equivalent to each other for different p . This dissertation is motivated by the recent result by C. E. Shin and Q. Sun (Journal ofFunctional Analysis, 256(2009), 2417-2439), where the lp-stability of infinite matrices in the Gohberg-Baskakov-Sjostrand class are proved to be equivalent to each other for different p. In the dissertation, for an infinite matrix having certain off-diagonal decay, its weighted lp-stability for different p are proved to be equivalent to each other and hence a result by Shin and Sun is generalized.<br>Ph.D.<br>Department of Mathematics<br>Sciences<br>Mathematics PhD

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Shi, Qiling. "Weighted l̳ p̳-stability for localized infinite matrices." Orlando, Fla. : University of Central Florida, 2008. http://purl.fcla.edu/fcla/etd/CFE0002685.

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Catalano, Riccardo R. [Verfasser]. "On Weighted Random Band-Matrices with Dependences / Riccardo R. Catalano." Hagen : Fernuniversität Hagen, 2016. http://d-nb.info/1102937126/34.

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Meinke, Ashley Marie. "Fibonacci Numbers and Associated Matrices." Kent State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=kent1310588704.

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Fasi, Massimiliano. "Weighted geometric mean of large-scale matrices: numerical analysis and algorithms." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/8274/.

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Computing the weighted geometric mean of large sparse matrices is an operation that tends to become rapidly intractable, when the size of the matrices involved grows. However, if we are not interested in the computation of the matrix function itself, but just in that of its product times a vector, the problem turns simpler and there is a chance to solve it even when the matrix mean would actually be impossible to compute. Our interest is motivated by the fact that this calculation has some practical applications, related to the preconditioning of some operators arising in domain decomposition of elliptic problems. In this thesis, we explore how such a computation can be efficiently performed. First, we exploit the properties of the weighted geometric mean and find several equivalent ways to express it through real powers of a matrix. Hence, we focus our attention on matrix powers and examine how well-known techniques can be adapted to the solution of the problem at hand. In particular, we consider two broad families of approaches for the computation of f(A) v, namely quadrature formulae and Krylov subspace methods, and generalize them to the pencil case f(A\B) v. Finally, we provide an extensive experimental evaluation of the proposed algorithms and also try to assess how convergence speed and execution time are influenced by some characteristics of the input matrices. Our results suggest that a few elements have some bearing on the performance and that, although there is no best choice in general, knowing the conditioning and the sparsity of the arguments beforehand can considerably help in choosing the best strategy to tackle the problem.

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Jin, Esther Yanfei. "Estrutura de vizinhanças espaciais nos modelos autorregressivos e de médias móveis espaço-temporais STARMA." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-24072017-194839/.

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O objetivo deste trabalho é comparar as estruturas de vizinhanças espaciais ou matrizes de pesos espaciais da classe de modelos autorregressivos e de médias móveis espaço-temporais (STARMA). O modelo STARMA é empregado para descrever dados de séries temporais espacialmente localizados, ele é caracterizado pela dependência linear defasada tanto no espaço quanto no tempo. Foram realizadas simulações utilizando vários modelos de covariância espaço-temporal para comparar diferentes estruturas de construção da matriz de pesos espaciais com a finalidade de identificar a melhor matriz. As matrizes espaciais com pesos exponenciais apresentaram os melhores desempenhos de ajuste dos modelos STAR; e mostram uma estabilidade em relação à medida de ajuste. Por fim para ilustração, será ajustado um modelo STARMA para um conjunto de dados mensais do índice FIPEZAP de preço imobiliário de venda para apartamentos de dois dormitórios de seis cidades metropolitanas de São Paulo.<br>The objective of this work is to compare spatial neighborhoods structures, or the same as spatial weights matrices of the class of space-time autoregressive and moving average models STARMA. The STARMA model is used to describe spatially localized time series datas, it is characterized by the linear dependence lagged both in space and time. Simulations were performed using several space-time covariance models to compare different structures of construction of the weight matrix with the purpose of identifying the best matrix. The spatial matrices with exponential weights presented the best adjustment performances of the STAR models ans showed a stability in relation to the adjustment measure. Finally, for illustration, a STARMA model will be adjusted for a set of monthly data of the FIPEZAP real estate price index for two bedroom apartments in six metropolitan cities of São Paulo.

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Chen, Zihao. "Forecasting realized covariance matrices: New methods to improve financial decision making." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/235363/1/10393480%2BZihao%2BChen%2BThesis%281%29.pdf.

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This thesis consists of three studies that centre around forecasting realised volatility based on high-frequency financial data. Accurate volatility forecasts are used extensively in many financial applications. The methods used here draw on econometric models and machine learning techniques. The empirical studies are based on fifty stocks and two stock indices. The thesis has established new perspectives on forecasting the realised volatility asset returns to improve financial decision-making.

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Books on the topic "Weight matrices"

1

Brugha, Cathal M. Extracting consistent weight ratio matrices from inconsistent judgement matrices in Ahp. University College Dublin, Dept of Management Information Systems, 1995.

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2

Fischer, Bernd. On adaptive weighted polynomial preconditioning for Hermitian positive definite matrices. Research Institute for Advanced Computer Science, NASA Ames Research Center, 1992.

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Metzler, Ed. On mosaical matrixes and the Metzler formula (Archives for mosaical metrology and mosaistics). Baalschem Press, 1997.

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Khoruzhenko, Boris, and Hans-Jurgen Sommers. Characteristic polynomials. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.19.

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This article considers characteristic polynomials and reviews a few useful results obtained in simple Gaussian models of random Hermitian matrices in the presence of an external matrix source. It first considers the products and ratio of characteristic polynomials before discussing the duality theorems for two different characteristic polynomials of Gaussian weights with external sources. It then describes the m-point correlation functions of the eigenvalues in the Gaussian unitary ensemble and how they are deduced from their Fourier transforms U(s1, … , sm). It also analyses the relation of the correlation function of the characteristic polynomials to the standard n-point correlation function using the replica and supersymmetric methods. Finally, it shows how the topological invariants of Riemann surfaces, such as the intersection numbers of the moduli space of curves, may be derived from averaged characteristic polynomials.

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Weighted graph based ordering techniques for preconditioned conjugate gradient methods. Research Institute for Advanced Computer Science, NASA Ames Research Center, 1994.

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Inverse Problems and Zero Forcing for Graphs. American Mathematical Society, 2022.

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Book chapters on the topic "Weight matrices"

1

Shekhar, Shashi, and Hui Xiong. "Spatial Weight Matrices." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1305.

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Zhang, Xiujun. "Position Weight Matrices." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_439.

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Bavaud, François. "Spatial Weights: Constructing Weight-Compatible Exchange Matrices from Proximity Matrices." In Geographic Information Science. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11593-1_6.

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Borre, Kai. "Block Elimination and Weight Matrices." In International Association of Geodesy Symposia. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56677-6_13.

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Smyth, W. Franklin. "Analysis of High Molecular Weight Analytes." In Analytical Chemistry of Complex Matrices. Vieweg+Teubner Verlag, 1996. http://dx.doi.org/10.1007/978-3-322-87182-4_8.

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Liefooghe, Aude, Hélène Touzet, and Jean-Stéphane Varré. "Large Scale Matching for Position Weight Matrices." In Combinatorial Pattern Matching. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11780441_36.

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Vajgl, Marek. "Reduced IFAM Weight Matrix Representation Using Sparse Matrices." In Advances in Fuzzy Logic and Technology 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66827-7_42.

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Smyth, W. Franklin. "Organic Trace Analysis of Low Molecular Weight Analytes in Environmental Samples and Biological Materials." In Analytical Chemistry of Complex Matrices. Vieweg+Teubner Verlag, 1996. http://dx.doi.org/10.1007/978-3-322-87182-4_7.

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Xiao, Xuanzhe, Zeng Li, Chuanlong Xie, and Fengwei Zhou. "Heavy-Tailed Regularization of Weight Matrices in Deep Neural Networks." In Artificial Neural Networks and Machine Learning – ICANN 2023. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44204-9_20.

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Stojanovic, Nikola. "Efficient Searching for Motifs in DNA Sequences Using Position Weight Matrices." In Biomedical Engineering Systems and Technologies. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18472-7_31.

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Conference papers on the topic "Weight matrices"

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Lee, Jung Hyun, Jeonghoon Kim, June Yong Yang, et al. "LRQ: Optimizing Post-Training Quantization for Large Language Models by Learning Low-Rank Weight-Scaling Matrices." In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.naacl-long.393.

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Makeev, Andrew, Dean Nguyen, Peter Mathews, Guillame Seon, Yuri Nikishkov, and Mark Robeson. "Analysis Methods Improving Confidence in Material Qualification for Laminated Composites." In Vertical Flight Society 72nd Annual Forum & Technology Display. The Vertical Flight Society, 2016. http://dx.doi.org/10.4050/f-0072-2016-11598.

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Fiber-reinforced composite materials are increasingly used in rotorcraft structures to reduce weight and improve efficiency. A major challenge, delaying the implementation of recently developed higher-performance materials that offer improved mechanical strength and stiffness at a lower weight, is the lack of efficient common material qualification. The current standard practices are too costly and time-consuming. Due to low confidence in material allowables, the entire material qualification process, including numerous test methods and large test matrices, must be repeated for every seemingly minute change in the composite system. This work presents initial results of research activities under the National Rotorcraft Technology Center, focused on utilizing recent advances in understanding complex deformation and failure mechanisms of polymer-matrix composites towards the development of consolidated common analysis processes. These common processes will enable reduced material qualification test matrices potentially accommodating substitution of resin types and other modifications of the material systems. In particular, analysis methods, developed at the University of Texas Arlington Advanced Materials and Structures Lab, are verified on carbon-fiber reinforced / untoughened and toughened polymer-matrix composite material systems. Due to well-recognized susceptibility of polymeric composites to matrix-dominated failures, thorough verification of the analysis methods using a range of the matrix types is required for improving confidence in the analysis-based material allowables accelerating material qualification.

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Pathak, Udayan, and Vikas Shingade. "Environmentally Friendly Controlled Cooling of Forgings as a Potential Replacement for Normalizing and Iso-annealing." In HT 2013, edited by B. Lynn Ferguson. ASM International, 2013. https://doi.org/10.31399/asm.cp.ht2013p0070.

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Abstract Forgings traditionally undergo normalizing or iso-annealing processes to achieve consistent hardness within controlled bands and to improve machinability. The need for these heat treatments stems primarily from the uncontrolled cooling of forgings after trimming operations. This paper demonstrates that similar results can be achieved through controlled cooling rates after trimming, with only minor differences in specific properties. The microstructure obtained through controlled cooling is predominantly coarse-grained, consisting of pearlite and ferrite matrices, contributing to improved machinability. Notably, the controlled cooling process offers potential energy savings of approximately 20 kg of oil per metric ton of net forging weight, with corresponding reductions in CO₂ emissions of up to 250 kg per metric ton. Implementation requires a specially designed cooling tunnel to regulate cooling rates precisely. This paper details the mechanical properties achieved for a carburizing grade steel, discusses necessary refinements to steel specifications, and outlines the process controls required to replace conventional normalizing/iso-annealing with controlled cooling effectively. Additionally, the paper presents the established cycles and cooling rates that produce optimal results in production environments.

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Giraud, Mathieu, and Jean-Stéphane Varre. "Parallel Position Weight Matrices Algorithms." In 2009 Eighth International Symposium on Parallel and Distributed Computing (ISPDC). IEEE, 2009. http://dx.doi.org/10.1109/ispdc.2009.31.

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Barzilai, Jonathan, and Boaz Golany . "An Axiomatic Framework for Aggregating Weights and Weight-Ratio Matrices." In The International Symposium on the Analytic Hierarchy Process. Creative Decisions Foundation, 1991. http://dx.doi.org/10.13033/isahp.y1991.009.

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WEAVER, D. C., C. T. WORKMAN, and G. D. STORMO. "MODELING REGULATORY NETWORKS WITH WEIGHT MATRICES." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 1998. http://dx.doi.org/10.1142/9789814447300_0011.

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"LINEAR--TIME MATCHING OF POSITION WEIGHT MATRICES." In International Conference on Bioinformatics. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0002750500660073.

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Jurado, Pol Grau, Xinyue Liang, and Saikat Chatterjee. "Deterministic Transform Based Weight Matrices for Neural Networks." In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022. http://dx.doi.org/10.1109/icassp43922.2022.9747256.

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Chen, Xin, Lingqiong Guo, Zhaocheng Fan, and Tao Jiang. "LEARNING POSITION WEIGHT MATRICES FROM SEQUENCE AND EXPRESSION DATA." In Proceedings of the CSB 2007 Conference. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2007. http://dx.doi.org/10.1142/9781860948732_0027.

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Kissel, Matthias, and Klaus Diepold. "Deep Convolutional Neural Networks with Sequentially Semiseparable Weight Matrices." In ESANN 2022 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Ciaco - i6doc.com, 2022. http://dx.doi.org/10.14428/esann/2022.es2022-21.

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Reports on the topic "Weight matrices"

1

Laber, Charles, Guilherme Lotufo, Austin Scircle, Jenifer Netchaev, and Anthony Bednar. Overview of microscale analytical methods for the quantitative detection of bioaccumulative contaminants in small tissue masses. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48190.

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For many bioaccumulation studies, generation of large sample masses of exposed organisms is challenging or even prohibitive. Therefore, the use of smaller sample masses for analysis without compromising data quality or quantitative level achieved is desirable. To this end, a variety of microanalytical procedures have been developed that used 1 g or less of tissue to address specific experimental challenges. However, these methods have not been systematically evaluated or published. The present work evaluates the current state of the microanalytical methods reported and identifies additional needs that would benefit US Army Corps of Engineers (USACE) research and navigation dredging programs. Discussions with commercial laboratories revealed that they typically do not accept small sample masses and require individual sample masses ranging from 10 to 20 g wet weight of tissue per analysis. If they do analyze a small mass sample, they routinely do not modify their standard process, resulting in detection and reporting limits orders of magnitude higher; therefore, essentially useless nondetect data are generated for regulatory decisions. To address the lack of commercial availability of microanalytical methods, we recommend pursuing method development and subsequent validation of microscale extraction and analysis of a variety of common contaminant compounds in tissue matrices.

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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 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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