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Dissertations / Theses on the topic 'Radial basis Kernel'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Choy, Kin-yee, and 蔡建怡. "On modelling using radial basis function networks with structure determined by support vector regression." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B29329619.

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2

McWhorter, Samuel P. "Fundamental Issues in Support Vector Machines." Thesis, University of North Texas, 2014. https://digital.library.unt.edu/ark:/67531/metadc500155/.

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This dissertation considers certain issues in support vector machines (SVMs), including a description of their construction, aspects of certain exponential kernels used in some SVMs, and a presentation of an algorithm that computes the necessary elements of their operation with proof of convergence. In its first section, this dissertation provides a reasonably complete description of SVMs and their theoretical basis, along with a few motivating examples and counterexamples. This section may be used as an accessible, stand-alone introduction to the subject of SVMs for the advanced undergraduate

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3

Kianifar, Mohammed R., and I. Felician Campean. "Performance evaluation of metamodelling methods for engineering problems: towards a practitioner guide." Springer, 2019. http://hdl.handle.net/10454/17192.

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Yes<br>Metamodelling or surrogate modelling techniques are frequently used across the engineering disciplines in conjunction with expensive simulation models or physical experiments. With the proliferation of metamodeling techniques developed to provide enhanced performance for specific problems, and the wide availability of a diverse choice of tools in engineering software packages, the engineering task of selecting a robust metamodeling technique for practical problems is still a challenge. This research introduces a framework for describing the typology of engineering problems, in terms of

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4

Wang, Tianyi. "Trajectory Similarity Based Prediction for Remaining Useful Life Estimation." University of Cincinnati / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1282574910.

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5

Manoharan, Madhu. "Evaluation of a neural network for formulating a semi-empirical variable kernel BRDF model." Master's thesis, Mississippi State : Mississippi State University, 2005. http://library.msstate.edu/content/templates/?a=72.

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6

Kohram, Mojtaba. "Experiments with Support Vector Machines and Kernels." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1378112059.

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7

Zhao, Yangzhang. "Multilevel sparse grid kernels collocation with radial basis functions for elliptic and parabolic problems." Thesis, University of Leicester, 2017. http://hdl.handle.net/2381/39148.

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Radial basis functions (RBFs) are well-known for the ease implementation as they are the mesh-free method [31, 37, 71, 72]. In this thesis, we modify the multilevel sparse grid kernel interpolation (MuSIK) algorithm proposed in [48] for use in Kansa’s collocation method (referred to as MuSIK-C) to solve elliptic and parabolic problems. The curse of dimensionality is a significant challenge in high dimension approximation. A full grid collocation method requires O(Nd) nodal points to construct an approximation; here N is the number of nodes in one direction and d means the dimension. However, t

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8

Chen, Yuan-Chia, and 陳原嘉. "Reproducing Kernel Enhanced Local Radial Basis Collocation Method for Solving Inverse Problems." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/2558uh.

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碩士<br>國立交通大學<br>土木工程系所<br>107<br>As inverse problems have been known for the incomplete boundary conditions, how to solve it effectively remains a challenging task in the field of computational mechanics. Although the radial basis collocation method has exponential convergence rate, the resulting discrete systems are full matrices and thus have ill-conditioned systems. In contrast, the reproducing kernel collocation method has algebraic convergence rate, but the resulting systems are more stable compared to the ones obtained by the global approximation. As such, this work introduces the locali

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9

Chiao, Kai-chieh, and 喬凱杰. "Compare the Behavior of Radial Basis Function Neural Network and Differential Reproducing kernel Approximation Method." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/37138327103090944403.

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碩士<br>國立臺灣科技大學<br>營建工程系<br>96<br>This study mainly examines the theory of Radial Basis Function (RBF) and Differential Reproducing kernel Approximation Method (DRKM), and compares the differences between them. The entire network calculation of RBF is controlled by the central points. There are two techniques to simulate the procedure: First, select the central point set randomly: to carry out the analysis by controlling the number of central points. Second, select the central points by Orthogonal Least Squares: to carry out the analysis by allocating the tolerance of errors. DKSM controls its

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10

"Properties of Divergence-Free Kernel Methods for Approximation and Solution of Partial Differential Equations." Doctoral diss., 2016. http://hdl.handle.net/2286/R.I.39449.

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abstract: Divergence-free vector field interpolants properties are explored on uniform and scattered nodes, and also their application to fluid flow problems. These interpolants may be applied to physical problems that require the approximant to have zero divergence, such as the velocity field in the incompressible Navier-Stokes equations and the magnetic and electric fields in the Maxwell's equations. In addition, the methods studied here are meshfree, and are suitable for problems defined on complex domains, where mesh generation is computationally expensive or inaccurate, or for problems wh

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11

Müller, Stefan. "Komplexität und Stabilität von kernbasierten Rekonstruktionsmethoden." Doctoral thesis, 2009. http://hdl.handle.net/11858/00-1735-0000-0006-B3BA-E.

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12

Schoelkopf, B., K. Sung, C. Burges, et al. "Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers." 1996. http://hdl.handle.net/1721.1/7180.

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The Support Vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special cases. In the RBF case, the SV algorithm automatically determines centers, weights and threshold such as to minimize an upper bound on the expected test error. The present study is devoted to an experimental comparison of these machines with a classical approach, where the centers are determined by $k$--means clustering and the weights are found using error backpropagati

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13

Rieger, Christian. "Sampling Inequalities and Applications." Doctoral thesis, 2008. http://hdl.handle.net/11858/00-1735-0000-0006-B3B9-0.

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14

Lin, Bo-Hong, and 林伯鴻. "Multispectral Brain Magnetic Resonance Image Analysis Using Weighted Radial Basis Function Kernels for Support Vector Machine." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/06175701365265737413.

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碩士<br>國立中興大學<br>通訊工程研究所<br>99<br>In this thesis, we will extend the RBF kernel of support vector machine (SVM) to weighted RBF kernel. The default coefficient of RBF kernel is an identity matrix with 0.5. This study will explore the correlation matrix, the covariance matrix and the covariance matrix by image itself on the use of the RBF kernel, and with different training samples which were randomly selected. Its main purpose is that we can select an appropriate RBF kernel who base on the statistical properties of image itself. Two major challenging issues arise in SVM: one is how to choice a

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15

Martins, Fernando Manuel Pires. "An implementation of flexible RBF neural networks." Master's thesis, 2009. http://hdl.handle.net/10451/5482.

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Tese de mestrado, Informática, Universidade de Lisboa, Faculdade de Ciências, 2009<br>Sempre que o trabalho de investigação resulta numa nova descoberta, a comunidade científica, e o mundo em geral, enriquece. Mas a descoberta científica per se não é suficiente. Para beneficio de todos, é necessário tornar estas inovações acessíveis através da sua fácil utilização e permitindo a sua melhoria, potenciando assim o progresso científico. Uma nova abordagem na modelação de núcleos em redes neuronais com Funções de Base Radial (RBF) foi proposta por Falção et al. em Flexible Kernels for RBF Networks

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