Relevant bibliographies by topics / Kernel parameter estimation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Kernel parameter estimation'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Kernel parameter estimation"

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Liu, Qing, David Pitt, Xibin Zhang, and Xueyuan Wu. "A Bayesian Approach to Parameter Estimation for Kernel Density Estimation via Transformations." Annals of Actuarial Science 5, no. 2 (2011): 181–93. http://dx.doi.org/10.1017/s1748499511000030.

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AbstractIn this paper, we present a Markov chain Monte Carlo (MCMC) simulation algorithm for estimating parameters in the kernel density estimation of bivariate insurance claim data via transformations. Our data set consists of two types of auto insurance claim costs and exhibits a high-level of skewness in the marginal empirical distributions. Therefore, the kernel density estimator based on original data does not perform well. However, the density of the original data can be estimated through estimating the density of the transformed data using kernels. It is well known that the performance
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Elie, Romuald. "Double Kernel Estimation of Sensitivities." Journal of Applied Probability 46, no. 3 (2009): 791–811. http://dx.doi.org/10.1239/jap/1253279852.

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In this paper we address the general issue of estimating the sensitivity of the expectation of a random variable with respect to a parameter characterizing its evolution. In finance, for example, the sensitivities of the price of a contingent claim are called the Greeks. A new way of estimating the Greeks has recently been introduced in Elie, Fermanian and Touzi (2007) through a randomization of the parameter of interest combined with nonparametric estimation techniques. In this paper we study another type of estimator that turns out to be closely related to the score function, which is well k
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Elie, Romuald. "Double Kernel Estimation of Sensitivities." Journal of Applied Probability 46, no. 03 (2009): 791–811. http://dx.doi.org/10.1017/s002190020000588x.

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In this paper we address the general issue of estimating the sensitivity of the expectation of a random variable with respect to a parameter characterizing its evolution. In finance, for example, the sensitivities of the price of a contingent claim are called the Greeks. A new way of estimating the Greeks has recently been introduced in Elie, Fermanian and Touzi (2007) through a randomization of the parameter of interest combined with nonparametric estimation techniques. In this paper we study another type of estimator that turns out to be closely related to the score function, which is well k
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Hovda, Sigve. "Properties of Transmetric Density Estimation." International Journal of Statistics and Probability 5, no. 3 (2016): 63. http://dx.doi.org/10.5539/ijsp.v5n3p63.

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Transmetric density estimation is a generalization of kernel density estimation that is proposed in Hovda(2014) and Hovda (2016), This framework involves the possibility of making assumptions on the kernel of the distribution to improve convergence orders and to reduce the number of dimensions in the graphical display. In this paper we show that several state-of-the-art nonparametric, semiparametric and even parametric methods are special cases of this formulation, meaning that there is a unified approach. Moreover, it is shown that parameters can be trained using unbiased cross-validation. Wh
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Baszczyńska, Aleksandra Katarzyna. "One Value of Smoothing Parameter vs Interval of Smoothing Parameter Values in Kernel Density Estimation." Acta Universitatis Lodziensis. Folia Oeconomica 6, no. 332 (2018): 73–86. http://dx.doi.org/10.18778/0208-6018.332.05.

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Ad hoc methods in the choice of smoothing parameter in kernel density estimation, al­though often used in practice due to their simplicity and hence the calculated efficiency, are char­acterized by quite big error. The value of the smoothing parameter chosen by Silverman method is close to optimal value only when the density function in population is the normal one. Therefore, this method is mainly used at the initial stage of determining a kernel estimator and can be used only as a starting point for further exploration of the smoothing parameter value. This paper pre­sents ad hoc methods for
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Barbeito, Inés, and Ricardo Cao. "Computationally Efficient Bootstrap Expressions for Bandwidth Selection in Nonparametric Curve Estimation." Proceedings 2, no. 18 (2018): 1164. http://dx.doi.org/10.3390/proceedings2181164.

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Bootstrap methods are used for bandwidth selection in: (1) nonparametric kernel density estimation with dependent data (smoothed stationary bootstrap and smoothed moving blocks bootstrap), and (2) nonparametric kernel hazard rate estimation (smoothed bootstrap). In these contexts, four new bandwidth parameter selectors are proposed based on closed bootstrap expressions of the MISE of the kernel density estimator (case 1) and two approximations of the kernel hazard rate estimation (case 2). These expressions turn out to be very useful since Monte Carlo approximation is no longer needed. Finally
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Hwang, TaeHyun, Rhee Man Kil, Hyun Ku Lee, et al. "Estimation of Jamming Parameters based on Gaussian Kernel Function Networks." Journal of the Korea Institute of Military Science and Technology 23, no. 1 (2020): 1–10. https://doi.org/10.9766/kimst.2020.23.1.001.

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Effective jamming in electronic warfare depends on proper jamming technique selection and jamming parameter estimation. For this purpose, this paper proposes a new method of estimating jamming parameters using Gaussian kernel function networks. In the proposed approach, a new method of determining the optimal structure and parameters of Gaussian kernel function networks is proposed. As a result, the proposed approach estimates the jamming parameters in a reliable manner and outperforms other methods such as the DNN(Deep Neural Network) and SVM(Support Vector Machine) estimation models.
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Schmid, H., M. P. Nash, A. A. Young, O. Röhrle, and P. J. Hunter. "A Computationally Efficient Optimization Kernel for Material Parameter Estimation Procedures." Journal of Biomechanical Engineering 129, no. 2 (2006): 279–83. http://dx.doi.org/10.1115/1.2540860.

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Estimating material parameters is an important part in the study of soft tissue mechanics. Computational time can easily run to days, especially when all available experimental data are taken into account. The material parameter estimation procedure is examplified on a set of homogeneous simple shear experiments to estimate the orthotropic constitutive parameters of myocardium. The modification consists of changing the traditional least-squares approach to a weighted least-squares. This objective function resembles a L2-norm type integral which is approximated using Gaussian quadrature. This r
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Chandra, Novita Eka, Sri Haryatmi, and Zulaela Zulaela. "REGRESI NONPARAMETRIK KERNEL ADJUSTED." Jurnal Ilmiah Matematika dan Pendidikan Matematika 7, no. 1 (2015): 1. http://dx.doi.org/10.20884/1.jmp.2015.7.1.2894.

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Nadaraya Watson's kernel adjusted regression estimator is an estimator whose kernel is taken from the family of scale-location associated with the classical kernel density estimator. Based on these estimator, it can be obtained optimal bandwith and scale parameter. This estimator gives a better estimation results compared with Naradaya Watson's classical kernel regression estimator. This is proven by the small grade MSE which is given by this estimator.
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Lakhdar, Yissam, and El Hassan Sbai. "Online Variable Kernel Estimator." International Journal of Operations Research and Information Systems 8, no. 1 (2017): 58–92. http://dx.doi.org/10.4018/ijoris.2017010104.

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In this work, the authors propose a novel method called online variable kernel estimation of the probability density function (pdf). This new online estimator combines the characteristics and properties of two estimators namely nearest neighbors estimator and the Parzen-Rosenblatt estimator. Their approach allows a compact online adaptation of the estimated probability density function from the new arrival data. The performance of the online variable kernel estimator (OVKE) depends on the choice of the bandwidth. The authors present in this article a new technique for determining the optimal s
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Dissertations / Theses on the topic "Kernel parameter estimation"

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Amezziane, Mohamed. "SMOOTHING PARAMETER SELECTION IN NONPARAMETRIC FUNCTIONAL ESTIMATION." Doctoral diss., University of Central Florida, 2004. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3488.

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This study intends to build up new techniques for how to obtain completely data-driven choices of the smoothing parameter in functional estimation, within the confines of minimal assumptions. The focus of the study will be within the framework of the estimation of the distribution function, the density function and their multivariable extensions along with some of their functionals such as the location and the integrated squared derivatives.<br>Ph.D.<br>Department of Mathematics<br>Arts and Sciences<br>Mathematics
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Wang, Xing. "Time Dependent Kernel Density Estimation: A New Parameter Estimation Algorithm, Applications in Time Series Classification and Clustering". Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6425.

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The Time Dependent Kernel Density Estimation (TDKDE) developed by Harvey & Oryshchenko (2012) is a kernel density estimation adjusted by the Exponentially Weighted Moving Average (EWMA) weighting scheme. The Maximum Likelihood Estimation (MLE) procedure for estimating the parameters proposed by Harvey & Oryshchenko (2012) is easy to apply but has two inherent problems. In this study, we evaluate the performances of the probability density estimation in terms of the uniformity of Probability Integral Transforms (PITs) on various kernel functions combined with different preset numbers. Furthermor
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Kayhan, Belgin. "Parameter Estimation In Generalized Partial Linear Modelswith Tikhanov Regularization." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612530/index.pdf.

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Regression analysis refers to techniques for modeling and analyzing several variables in statistical learning. There are various types of regression models. In our study, we analyzed Generalized Partial Linear Models (GPLMs), which decomposes input variables into two sets, and additively combines classical linear models with nonlinear model part. By separating linear models from nonlinear ones, an inverse problem method Tikhonov regularization was applied for the nonlinear submodels separately, within the entire GPLM. Such a particular representation of submodels provides both a better accurac
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Dion, Charlotte. "Estimation non-paramétrique de la densité de variables aléatoires cachées." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAM031/document.

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Cette thèse comporte plusieurs procédures d'estimation non-paramétrique de densité de probabilité.Dans chaque cas les variables d'intérêt ne sont pas observées directement, ce qui est une difficulté majeure.La première partie traite un modèle linéaire mixte où des observations répétées sont disponibles.La deuxième partie s'intéresse aux modèles d'équations différentielles stochastiques à effets aléatoires. Plusieurs trajectoires sont observées en temps continu sur un intervalle de temps commun.La troisième partie se place dans un contexte de bruit multiplicatif.Les différentes parties de cette
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Libengue, Dobele-kpoka Francial Giscard Baudin. "Méthode non-paramétrique des noyaux associés mixtes et applications." Thesis, Besançon, 2013. http://www.theses.fr/2013BESA2007/document.

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Nous présentons dans cette thèse, l'approche non-paramétrique par noyaux associés mixtes, pour les densités àsupports partiellement continus et discrets. Nous commençons par rappeler d'abord les notions essentielles d'estimationpar noyaux continus (classiques) et noyaux associés discrets. Nous donnons la définition et les caractéristiques desestimateurs à noyaux continus (classiques) puis discrets. Nous rappelons aussi les différentes techniques de choix deparamètres de lissage et nous revisitons les problèmes de supports ainsi qu'une résolution des effets de bord dans le casdiscret. Ensuite,
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El, Heda Khadijetou. "Choix optimal du paramètre de lissage dans l'estimation non paramétrique de la fonction de densité pour des processus stationnaires à temps continu." Thesis, Littoral, 2018. http://www.theses.fr/2018DUNK0484/document.

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Les travaux de cette thèse portent sur le choix du paramètre de lissage dans le problème de l'estimation non paramétrique de la fonction de densité associée à des processus stationnaires ergodiques à temps continus. La précision de cette estimation dépend du choix de ce paramètre. La motivation essentielle est de construire une procédure de sélection automatique de la fenêtre et d'établir des propriétés asymptotiques de cette dernière en considérant un cadre de dépendance des données assez général qui puisse être facilement utilisé en pratique. Cette contribution se compose de trois parties. L
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Ortiz, Joseph Christian, and Joseph Christian Ortiz. "Estimation of Kinetic Parameters From List-Mode Data Using an Indirect Approach." Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/621785.

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This dissertation explores the possibility of using an imaging approach to model classical pharmacokinetic (PK) problems. The kinetic parameters which describe the uptake rates of a drug within a biological system, are parameters of interest. Knowledge of the drug uptake in a system is useful in expediting the drug development process, as well as providing a dosage regimen for patients. Traditionally, the uptake rate of a drug in a system is obtained via sampling the concentration of the drug in a central compartment, usually the blood, and fitting the data to a curve. In a system consisting o
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Diaz, José Ignacio Valencia. "Modelagem não-paramétrica da dinâmica da taxa de juros instantânea utilizando contratos futuros da taxa média dos depósitos interfinanceiros de 1 dia (DI1)." reponame:Repositório Institucional do FGV, 2013. http://hdl.handle.net/10438/11130.

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Submitted by José Ignacio Valencia Díaz (jivalenciadiaz@gmail.com) on 2013-09-17T00:13:33Z No. of bitstreams: 1 Dissertacao MPFE Jose Ignacio Valencia Diaz.pdf: 1741345 bytes, checksum: b45af943bf4f6e8a2a9963c07038d9dc (MD5)<br>Approved for entry into archive by Suzinei Teles Garcia Garcia (suzinei.garcia@fgv.br) on 2013-09-17T12:05:59Z (GMT) No. of bitstreams: 1 Dissertacao MPFE Jose Ignacio Valencia Diaz.pdf: 1741345 bytes, checksum: b45af943bf4f6e8a2a9963c07038d9dc (MD5)<br>Made available in DSpace on 2013-09-17T12:54:35Z (GMT). No. of bitstreams: 1 Dissertacao MPFE Jose Ignacio Valenci
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Muševič, Sašo. "Non-stationary sinusoidal analysis." Doctoral thesis, Universitat Pompeu Fabra, 2013. http://hdl.handle.net/10803/123809.

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Muchos tipos de señales que encontramos a diario pertenecen a la categoría de sinusoides no estacionarias. Una gran parte de esas señales son sonidos que presentan una gran variedad de características: acústicos/electrónicos, sonidos instrumentales harmónicos/impulsivos, habla/canto, y la mezcla de todos ellos que podemos encontrar en la música. Durante décadas la comunidad científica ha estudiado y analizado ese tipo de señales. El motivo principal es la gran utilidad de los avances científicos en una gran variedad de áreas, desde aplicaciones médicas, financiera y ópticas, a procesado de rad
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Kowolowski, Alexander. "Vývoj moderních akustických parametrů kvantifikujících hypokinetickou dysartrii." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-401990.

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This work deals with designing and testing of new acoustic features for analysis of dysprosodic speech occurring in hypokinetic dysarthria patients. 41 new features for dysprosody quantification (describing melody, loudness, rhythm and pace) are presented and tested in this work. New features can be divided into 7 groups. Inside the groups, features vary by the used statistical values. First four groups are based on absolute differences and cumulative sums of fundamental frequency and short-time energy of the signal. Fifth group contains features based on multiples of this fundamental frequenc
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Books on the topic "Kernel parameter estimation"

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Ullah, A. Nonparametric kernel estimation of econometric parameters. College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1987.

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Chance, Kelly, and Randall V. Martin. Data Fitting. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199662104.003.0011.

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This chapter explores several of the most common and useful approaches to atmospheric data fitting as well as the process of using air mass factors to produce vertical atmospheric column abundances from line-of-sight slant columns determined by data fitting. An atmospheric spectrum or other type of atmospheric sounding is usually fitted to a parameterized physical model by minimizing a cost function, usually chi-squared. Linear fitting, when the model of the measurements is linear in the model parameters is described, followed by the more common nonlinear fitting case. For nonlinear fitting, t
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Book chapters on the topic "Kernel parameter estimation"

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Gómez-Orozco, V., J. Cuellar, Hernán F. García, et al. "A Kernel-Based Approach for DBS Parameter Estimation." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52277-7_20.

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Ohn, Syng-Yup, Ha-Nam Nguyen, and Sung-Do Chi. "Evolutionary Parameter Estimation Algorithm for Combined Kernel Function in Support Vector Machine." In Content Computing. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30483-8_59.

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Kang, Min-Jae, Chang-Jin Boo, Ho-Chan Kim, and Jacek M. Zurada. "Estimating Soil Parameters Using the Kernel Function." In Computational Science and Its Applications – ICCSA 2010. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12165-4_9.

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Rachdi, Mustapha, Ali Laksaci, Ali Hamié, Jacques Demongeot, and Idir Ouassou. "Curves Classification by Using a Local Likelihood Function and Its Practical Usefulness for Real Data." In Fuzzy Systems and Data Mining VI. IOS Press, 2020. http://dx.doi.org/10.3233/faia200691.

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We extend the classical approach in supervised classification based on the local likelihood estimation to the functional covariates case. The estimation procedure of the functional parameter (slope parameter) in the linear model when the covariate is of functional kind is investigated. We show, on simulated as well on real data, that classification error rates estimated using test samples, and the estimation procedure by local likelihood seem to lead to better estimators than the classical kernel estimation. In addition, this approach is no longer assuming that the linear predictors have a spe
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Taha Mohammed Ali, Haithem, and Sameera Abdulsalam Othman. "A New Approach of Power Transformations in Functional Non-Parametric Temperature Time Series." In Time Series Analysis - New Insights [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.105832.

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In nonparametric analyses, many authors indicate that the kernel density functions work well when the variable is close to the Gaussian shape. This chapter interest is on the improvement the forecastability of the functional nonparametric time series by using a new approach of the parametric power transformation. The choice of the power parameter in this approach is based on minimizing the mean integrated square error of kernel estimation. Many authors have used this criterion in estimating density under the assumption that the original data follow a known probability distribution. In this cha
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Xing, Daitao, and Anthony Tzes. "Deformable Correlation Networks for Aerial Object Tracking and Segmentation." In Drones - Various Applications [Working Title]. IntechOpen, 2024. http://dx.doi.org/10.5772/intechopen.1003777.

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While recent object trackers, which employ segmentation methods for bounding box estimation, have achieved significant advancements in tracking accuracy, they are still limited in their ability to accommodate geometric transformations. This limitation results in poor performance over long sequences in aerial object-tracking applications. To mitigate this problem, we propose a novel real-time tracking framework consisting of deformation modules. These modules model geometric variations and appearance changes at different levels for segmentation purposes. Specifically, the proposal deformation m
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Zhang, Tianze. "Power System Uncertainty Modeling Based on Gaussian Process." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia231436.

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In order to solve the problem of low accuracy of parameter estimation in expectation maximization algorithm, a modeling method based on Gaussian component number reduction is proposed. Taking the nonparametric kernel density estimation results as the base Gaussian mixture model, the Gaussian mixture model with any number of Gaussian components can be established by reducing the number of Gaussian components by using the density-preserving hierarchical expectation maximization algorithm, which overcomes the problem that the expectation maximization algorithm has low parameter estimation accurac
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Ramanna, C. K., and G. R. Dodagoudar. "Effect of Epicenter Data Inconsistency in Determining Bandwidth and Its Subsequent Use in Hazard Analysis for Chennai Using Kernel Smoothing Approach." In Civil and Environmental Engineering. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9619-8.ch065.

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The most important parameter in the kernel density estimator is the bandwidth or spread or window width. The bandwidth of the kernel density estimator, which follows the power law, is determined using the nearest neighborhood technique for the earthquake catalog which is divided into bins. For reliable hazard estimates, the magnitude bins used in developing the power law and estimating the spatial activity rate density function should be the same. It is important that consistency be maintained between the earthquake epicenters used in determining the bandwidth and the epicenters to which the b
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Alzghool, Raed. "ARCH and GARCH Models: Quasi-Likelihood and Asymptotic Quasi-Likelihood Approaches." In Linear and Non-Linear Financial Econometrics -Theory and Practice [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.93726.

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This chapter considers estimation of autoregressive conditional heteroscedasticity (ARCH) and the generalized autoregressive conditional heteroscedasticity (GARCH) models using quasi-likelihood (QL) and asymptotic quasi-likelihood (AQL) approaches. The QL and AQL estimation methods for the estimation of unknown parameters in ARCH and GARCH models are developed. Distribution assumptions are not required of ARCH and GARCH processes by QL method. Nevertheless, the QL technique assumes knowing the first two moments of the process. However, the AQL estimation procedure is suggested when the conditi
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Kul, Seda, Muhammet Fatih Aslan, and Suleyman Sungur Tezcan. "Core Loss Estimation for Three Phase Transformer Based on GPR and FEA." In Advances in Transdisciplinary Engineering. IOS Press, 2024. http://dx.doi.org/10.3233/atde240671.

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This study used the Gaussian Process Regression (GPR) method to predict the core losses of the Finite Element Analysis (FEA) based dry-type three-phase transformer. In the estimation and analysis processes, the core area Ac, primary excitation voltage Vp and the primary winding number of turns Np are used as three input parameters. GPR is a powerful machine learning method for such low-featured data and provides a Bayesian-based regression capable of measuring uncertainty in predictions. The data generated in the ANSYS/MAXWELL environment for core loss estimation is chosen at random using the
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Conference papers on the topic "Kernel parameter estimation"

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B. Nelson, J., R. Damper, S. Gunn, and B. Guo. "Signal Theory for SVM Kernel Parameter Estimation." In 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing. IEEE, 2006. http://dx.doi.org/10.1109/mlsp.2006.275539.

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Nataraj, Gopal, Jon-Fredrik Nielsen, and Jeffrey A. Fessler. "Dictionary-free MRI parameter estimation via kernel ridge regression." In 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). IEEE, 2017. http://dx.doi.org/10.1109/isbi.2017.7950455.

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Pin, Gilberto, Andrea Assalone, Marco Lovera, and Thomas Parisini. "Kernel-based non-asymptotic parameter estimation of continuous-time systems." In 2012 IEEE 51st Annual Conference on Decision and Control (CDC). IEEE, 2012. http://dx.doi.org/10.1109/cdc.2012.6426854.

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Watanabe, Shinya, and Yukiyo Kimura. "A methodology using EMO for parameter estimation of SVM kernel function." In 2008 IEEE Conference on Soft Computing in Industrial Applications (SMCia). IEEE, 2008. http://dx.doi.org/10.1109/smcia.2008.5045962.

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Horiuchi, Keisuke, and Keisuke Kameyama. "Parameter Density Inheritance Using Kernel Density Estimation for Efficient CNN Learning." In 2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). IEEE, 2018. http://dx.doi.org/10.1109/isspit.2018.8642618.

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Kondo, Takaaki, and Yoshito Ohta. "A hyper-parameter estimation algorithm in kernel based regularization approach for system identification using Kautz kernels." In 2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE). IEEE, 2017. http://dx.doi.org/10.23919/sice.2017.8105743.

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Chowdhury, Souma, Ali Mehmani, and Achille Messac. "Concurrent Surrogate Model Selection (COSMOS) Based on Predictive Estimation of Model Fidelity." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-35358.

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One of the primary drawbacks plaguing wider acceptance of surrogate models is their low fidelity in general. This issue can be in a large part attributed to the lack of automated model selection techniques, particularly ones that do not make limiting assumptions regarding the choice of model types and kernel types. A novel model selection technique was recently developed to perform optimal model search concurrently at three levels: (i) optimal model type (e.g., RBF), (ii) optimal kernel type (e.g., multiquadric), and (iii) optimal values of hyper-parameters (e.g., shape parameter) that are con
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Liao, Ziyan, and Yunting Liu. "Topology and Line Parameter Identification Using Kernel Density Estimation in Distribution Networks." In 2024 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT). IEEE, 2024. http://dx.doi.org/10.1109/isgt59692.2024.10454242.

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Lee, Hyokyeong, and Rahul Singh. "Unsupervised kernel parameter estimation by constrained nonlinear optimization for clustering nonlinear biological data." In 2012 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2012. http://dx.doi.org/10.1109/bibm.2012.6392694.

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Fu, Yujia, Hongfeng Tao, and Huizhong Yang. "Simultaneous estimation of the number of principal components and kernel parameter in KPCA." In 2017 6th International Symposium on Advanced Control of Industrial Processes (AdCONIP). IEEE, 2017. http://dx.doi.org/10.1109/adconip.2017.7983771.

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