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Relevant bibliographies by topics / Quadratic discriminant function. eng

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Academic literature on the topic 'Quadratic discriminant function. eng'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Quadratic discriminant function. eng"

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Chen, Y., and B. J. Collier. "Characterizing Fabric End-Use by Fabric Physical Properties." Textile Research Journal 67, no. 4 (1997): 247–52. http://dx.doi.org/10.1177/004051759706700402.

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This paper introduces a statistical approach to predicting fabric end-use based on Kawabata kes-fb data determining fabric physical properties. The classification theory zbasis of discriminant analysis is discussed, and a way to establish classification criteria to characterize fabrics for clothing uses is demonstrated. The mathematical prediction model is described by a quadratic discriminant function that successfully classifies fabrics appropriate for suiting, blouses, and shirts.

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Ćwiklińska-Jurkowska, Małgorzata M. "Performance of Resampling Methods Based on Decision Trees, Parametric and Nonparametric Bayesian Classifiers for Three Medical Datasets." Studies in Logic, Grammar and Rhetoric 35, no. 1 (2013): 71–86. http://dx.doi.org/10.2478/slgr-2013-0045.

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Abstract The figures visualizing single and combined classifiers coming from decision trees group and Bayesian parametric and nonparametric discriminant functions show the importance of diversity of bagging or boosting combined models and confirm some theoretical outcomes suggested by other authors. For the three medical sets examined, decision trees, as well as linear and quadratic discriminant functions are useful for bagging and boosting. Classifiers, which do not show an increasing tendency for resubstitution errors in subsequent boosting deterministic procedures loops, are not useful for

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Müller, Wolfgang. "The mean square of the Dedekind zeta function in quadratic number fields." Mathematical Proceedings of the Cambridge Philosophical Society 106, no. 3 (1989): 403–17. http://dx.doi.org/10.1017/s0305004100068134.

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Let K be a quadratic number field with discriminant D. The aim of this paper is to study the mean square of the Dedekind zeta function ζK on the critical line, i.e.It was proved by Chandrasekharan and Narasimhan[1] that (1) is at most of order O(T(log T)2). As they noted at the end of their paper, it ‘would seem likely’ that (1) behaves asymptotically like a2T(log T)2, with some constant a2 depending on K. Applying a general mean value theorem for Dirichlet polynomials, one can actually proveThis may be done in just the same way as this general mean value theorem can be used to prove Ingham's

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Chikara, Rupesh Kumar, and Li-Wei Ko. "Prediction of Human Inhibition Brain Function with Inter-Subject and Intra-Subject Variability." Brain Sciences 10, no. 10 (2020): 726. http://dx.doi.org/10.3390/brainsci10100726.

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The stop signal task has been used to quantify the human inhibitory control. The inter-subject and intra-subject variability was investigated under the inhibition of human response with a realistic environmental scenario. In present study, we used a battleground scenario where a sniper-scope picture was the background, a target picture was a go signal, and a nontarget picture was a stop signal. The task instructions were to respond on the target image and inhibit the response if a nontarget image appeared. This scenario produced a threatening situation and endorsed the evaluation of how subjec

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Houshmand, Ali A. "Misclassification probabilities for quadratic discriminant function." Communications in Statistics - Simulation and Computation 22, no. 1 (1993): 81–98. http://dx.doi.org/10.1080/03610919308813082.

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Girko, V. L., and T. V. Pavlenko. "G-estimates of the quadratic discriminant function." Ukrainian Mathematical Journal 41, no. 12 (1989): 1469–73. http://dx.doi.org/10.1007/bf01056118.

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Kimura, Fumitaka, Kenji Takashina, Shinji Tsuruoka, and Yasuji Miyake. "Avoiding peaking phenomenon of the quadratic discriminant function." Systems and Computers in Japan 18, no. 9 (1987): 44–51. http://dx.doi.org/10.1002/scj.4690180905.

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Liu, C. L., H. Sako, and H. Fujisawa. "Discriminative Learning Quadratic Discriminant Function for Handwriting Recognition." IEEE Transactions on Neural Networks 15, no. 2 (2004): 430–44. http://dx.doi.org/10.1109/tnn.2004.824263.

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Dučinskas, K., and J. Šaltytė. "Quadratic Discriminant Analysis of Spatially Correlated Data." Nonlinear Analysis: Modelling and Control 6, no. 2 (2001): 15–28. http://dx.doi.org/10.15388/na.2001.6.1.15212.

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The problem of classification of the realisation of the stationary univariate Gaussian random field into one of two populations with different means and different factorised covariance matrices is considered. In such a case optimal classification rule in the sense of minimum probability of misclassification is associated with non-linear (quadratic) discriminant function. Unknown means and the covariance matrices of the feature vector components are estimated from spatially correlated training samples using the maximum likelihood approach and assuming spatial correlations to be known. Explicit

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Ganeslingam, S., A. Nanthakumar, and Siva Ganesh. "A comparison of quadratic discriminant function with discriminant function based on absolute deviation from the mean." Journal of Statistics and Management Systems 9, no. 2 (2006): 441–57. http://dx.doi.org/10.1080/09720510.2006.10701216.

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Dissertations / Theses on the topic "Quadratic discriminant function. eng"

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Simeão, Sandra Fiorelli de Almeida Penteado 1965. "Aplicativo computacional da função discriminante quadrática para utilização em ciências experimentais /." Botucatu : [s.n.], 2006. http://hdl.handle.net/11449/101776.

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Orientador: Carlos Roberto Padovani<br>Banca: Adriano Wagner Ballarin<br>Banca: Flávio Fekkari Aragon<br>Banca: José Carlos Martinez<br>Banca: Marie Oshiiwa<br>Resumo: Aspectos teóricos relacionados à Análise Discriminante Multivariada - Linear e Quadrática - foram discutidos, por meio de um extenso levantamento histórico da função discriminante, com seus primórdios no trabalho de Fisher e sua posterior evolução, enfocando o intenso desenvolvimento das técnicas classificatórias discriminantes com o advento dos computadores. Foi dada ênfase aos softwares estatísticos desenvolvidos para PC, que

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Simeão, Sandra Fiorelli de Almeida Penteado [UNESP]. "Aplicativo computacional da função discriminante quadrática para utilização em ciências experimentais." Universidade Estadual Paulista (UNESP), 2006. http://hdl.handle.net/11449/101776.

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Made available in DSpace on 2014-06-11T19:31:35Z (GMT). No. of bitstreams: 0 Previous issue date: 2006-12-19Bitstream added on 2014-06-13T21:02:54Z : No. of bitstreams: 1 simeao_sfap_dr_botfca.pdf: 899191 bytes, checksum: da6ed77a45734c278c56395d23c51cd0 (MD5)<br>Universidade Estadual Paulista (UNESP)<br>Aspectos teóricos relacionados à Análise Discriminante Multivariada - Linear e Quadrática - foram discutidos, por meio de um extenso levantamento histórico da função discriminante, com seus primórdios no trabalho de Fisher e sua posterior evolução, enfocando o intenso desenvolvimento das téc

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Padovani, Carlos Roberto Pereira 1975. "Aplicativo computacional para obtenção de probabilidades a priori de classificação errônea em experimentos agronômicos /." Botucatu : [s.n.], 2007. http://hdl.handle.net/11449/101872.

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Orientador: Flávio Ferrari Aragon<br>Banca: Adriano Wagner Ballarin<br>Banca: Luís Fernando Nicolosi Bravin<br>Banca: Rui Vieira de Moraes<br>Banca: Sandra Fiorelli de Almeida P. Simeão<br>Resumo: Nas Ciências Agronômicas, encontram-se várias situações em que são observadas diversas variáveis respostas nas parcelas ou unidades experimentais. Nestas situações, um caso de interesse prático à experimentação agronômica é o que considera a construção de regiões de similaridade entre as parcelas para a discriminação entre os grupos experimentais e ou para a classificação de novas unidades experiment

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Book chapters on the topic "Quadratic discriminant function. eng"

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Xu, Bo, Kaizhu Huang, Irwin King, Cheng-Lin Liu, Jun Sun, and Naoi Satoshi. "Graphical Lasso Quadratic Discriminant Function for Character Recognition." In Neural Information Processing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24965-5_84.

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Omachi, Shin’ichiro, Fang Sun, and Hirotomo Aso. "A New Approximation Method of the Quadratic Discriminant Function." In Advances in Pattern Recognition. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44522-6_62.

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Yang, Duan-Duan, Lian-Wen Jin, Jun-Xun Yin, Li-Xin Zhen, and Jian-Cheng Huang. "Kernel Modified Quadratic Discriminant Function for Facial Expression Recognition." In Advances in Machine Vision, Image Processing, and Pattern Analysis. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11821045_7.

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Singh, Aman, and Babita Pandey. "Diagnosis of Liver Disease by Using Least Squares Support Vector Machine Approach." In Intelligent Systems. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5643-5.ch081.

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A healthy liver leads to healthy life. In India, as well as in other parts of the world, liver disease is one of the principle areas of concern in medicine. For this study, diagnosis of liver disease is performed by deploying classification methods include linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), feed-forward neural network (FFNN) and support vector machine (SVM) based approaches. Experimental results concluded that SVM based approaches outperformed all other classification methods in terms of diagnostic accuracy rates. Furthermore, least squares support vector machine (LSSVM) with gaussian radial basis kernel function based machine learning approach had emerged as the as the best predictive model by reducing inefficiencies caused by false diagnosis. LSSVM also performed better than linear SVM, polynomial SVM, quadratic SVM and multilayer perceptron SVM despite the uneven variance in attribute values in the health examination data.

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Edixhoven, Bas. "Computing coefficients of modular forms." In Computational Aspects of Modular Forms and Galois Representations. Princeton University Press, 2011. http://dx.doi.org/10.23943/princeton/9780691142012.003.0015.

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This chapter applies the main result on the computation of Galois representations attached to modular forms of level one to the computation of coefficients of modular forms. It treats the case of the discriminant modular form, that is, the computation of Ramanujan's tau-function at primes, and then deals with the more general case of forms of level one and arbitrary weight k, reformulated as the computation of Hecke operators Tⁿ as ℤ-linear combinations of the Tᵢ with i < k = 12. The chapter gives an application to theta functions of even, unimodular positive definite quadratic forms over ℤ.

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Sun, Minghe. "Support Vector Machine Models for Classification." In Encyclopedia of Business Analytics and Optimization. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-5202-6.ch215.

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As machine learning techniques, support vector machines are quadratic programming models and are recent revolutionary development for classification analysis. Primal and dual formulations of support vector machine models for both two-class and multi-class classification are discussed. The dual formulations in high dimensional feature space using inner product kernels are emphasized. Nonlinear classification function or discriminant functions in high dimensional feature spaces can be constructed through the use of inner product kernels without actually mapping the data from the input space to the high dimensional feature spaces. Furthermore, the size of the dual formulation is independent of the dimension of the input space and independent of the kernels used. Two illustrative examples, one for two-class and the other for multi-class classification, are used to demonstrate the formulations of these SVM models.

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Conference papers on the topic "Quadratic discriminant function. eng"

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Zhang, Xu-Yao, and Cheng-Lin Liu. "Locally Smoothed Modified Quadratic Discriminant Function." In 2013 12th International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2013. http://dx.doi.org/10.1109/icdar.2013.11.

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Su, Tong-Hua, Cheng-Lin Liu, and Xu-Yao Zhang. "Perceptron Learning of Modified Quadratic Discriminant Function." In 2011 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2011. http://dx.doi.org/10.1109/icdar.2011.204.

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Chen, Jingnian, Li Xu, Xiang Zhao, and Shunxiang Hu. "Speeding up Discriminative Learning Quadratic Discriminant Function with sampling." In 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2012. http://dx.doi.org/10.1109/fskd.2012.6234013.

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Yang, D., D. Yang, L. Jin, et al. "Kernel Modified Quadratic Discriminant Function for Online Handwritten Chinese Characters Recognition." In Ninth International Conference on Document Analysis and Recognition (ICDAR 2007). IEEE, 2007. http://dx.doi.org/10.1109/icdar.2007.4378672.

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Neto, Anselmo Chaves, Isabella Andreczevski Chaves, Henrik C. Gregario, G. Vargas, Luis A. Paixao, and Emilio R. F. Neto. "Quality assessment of insulating oil in transformers with application of quadratic discriminant function." In 2013 IEEE PES Conference on Innovative Smart Grid Technologies (ISGT Latin America). IEEE, 2013. http://dx.doi.org/10.1109/isgt-la.2013.6554473.

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Zhou, Ming-Ke, Fei Yin, and Cheng-Lin Liu. "GPU-Based Fast Training of Discriminative Learning Quadratic Discriminant Function for Handwritten Chinese Character Recognition." In 2013 12th International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2013. http://dx.doi.org/10.1109/icdar.2013.172.

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