Relevant bibliographies by topics / Radial basis Kernel

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Academic literature on the topic 'Radial basis Kernel'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Radial basis Kernel"

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Singh, Himanshu. "Machine Learning Application of Generalized Gaussian Radial Basis Function and Its Reproducing Kernel Theory." Mathematics 12, no. 6 (2024): 829. http://dx.doi.org/10.3390/math12060829.

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Gaussian Radial Basis Function Kernels are the most-often-employed kernel function in artificial intelligence for providing the optimal results in contrast to their respective counterparts. However, our understanding surrounding the utilization of the Generalized Gaussian Radial Basis Function across different machine learning algorithms, such as kernel regression, support vector machines, and pattern recognition via neural networks is incomplete. The results delivered by the Generalized Gaussian Radial Basis Function Kernel in the previously mentioned applications remarkably outperforms those
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Caraka, Rezzy Eko, Hasbi Yasin, and Adi Waridi Basyiruddin. "Peramalan Crude Palm Oil (CPO) Menggunakan Support Vector Regression Kernel Radial Basis." Jurnal Matematika 7, no. 1 (2017): 43. http://dx.doi.org/10.24843/jmat.2017.v07.i01.p81.

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Recently, instead of selecting a kernel has been proposed which uses SVR, where the weight of each kernel is optimized during training. Along this line of research, many pioneering kernel learning algorithms have been proposed. The use of kernels provides a powerful and principled approach to modeling nonlinear patterns through linear patterns in a feature space. Another bene?t is that the design of kernels and linear methods can be decoupled, which greatly facilitates the modularity of machine learning methods. We perform experiments on real data sets crude palm oil prices for application and
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Almaiah, Mohammed Amin, Omar Almomani, Adeeb Alsaaidah, et al. "Performance Investigation of Principal Component Analysis for Intrusion Detection System Using Different Support Vector Machine Kernels." Electronics 11, no. 21 (2022): 3571. http://dx.doi.org/10.3390/electronics11213571.

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The growing number of security threats has prompted the use of a variety of security techniques. The most common security tools for identifying and tracking intruders across diverse network domains are intrusion detection systems. Machine Learning classifiers have begun to be used in the detection of threats, thus increasing the intrusion detection systems’ performance. In this paper, the investigation model for an intrusion detection systems model based on the Principal Component Analysis feature selection technique and a different Support Vector Machine kernels classifier is present. The imp
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Mohd Hatta, Noramalina, Zuraini Ali Shah, and Shahreen Kasim. "Evaluate the Performance of SVM Kernel Functions for Multiclass Cancer Classification." International Journal on Data Science 1, no. 1 (2020): 37–41. http://dx.doi.org/10.18517/ijods.1.1.37-41.2020.

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Multiclass cancer classification is basically one of the challenging fields in machine learning which a fast growing technology that use human behaviour as examples. Supervised classification such Support Vector Machine (SVM) has been used to classify the dataset on classification by its own function and merely known as kernel function. Kernel function has stated to have a problem especially in selecting their best kernels based on a specific datasets and tasks. Besides, there is an issue stated that the kernels function have a high impossibility to distribute the data in straight line. Here,
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Lin, Shaobo, Jinshan Zeng, and Zongben Xu. "Interpolation and Best Approximation for Spherical Radial Basis Function Networks." Abstract and Applied Analysis 2013 (2013): 1–5. http://dx.doi.org/10.1155/2013/206265.

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Within the conventional framework of a native space structure, a smooth kernel generates a small native space, and radial basis functions stemming from the smooth kernel are intended to approximate only functions from this small native space. In this paper, we embed the smooth radial basis functions in a larger native space generated by a less smooth kernel and use them to interpolate the samples. Our result shows that there exists a linear combination of spherical radial basis functions that can both exactly interpolate samples generated by functions in the larger native space and near best a
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Erkamim, Moh, Said Thaufik Rizaldi, Sepriano Sepriano, et al. "Data Sharing Technique for Electronic Health Record (EHR) Classification using Support Vector Machine Algorithm." Indonesian Journal of Artificial Intelligence and Data Mining 6, no. 1 (2023): 123. http://dx.doi.org/10.24014/ijaidm.v6i1.24794.

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The Electronic Health Record (EHR) integrates information about medical history in patients, complications, and history of drug use efficiently, which demands optimality and speed of service for efficiency and effectiveness of services, especially in determining outpatient and inpatient services on accurate patient history data. In efforts to improve data accuracy, this study combined the c, γ, and degree kernels in the Linear, Polynomial, and Radial Basis Function (RBF) kernels as well as data sharing techniques 10-fold cross-validation, k-medoids, and Hold- out (70 % 30%) resulted in superio
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Alida, Mufni, and Metty Mustikasari. "Rupiah Exchange Prediction of US Dollar Using Linear, Polynomial, and Radial Basis Function Kernel in Support Vector Regression." Jurnal Online Informatika 5, no. 1 (2020): 53–60. http://dx.doi.org/10.15575/join.v5i1.537.

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As a developing country, Indonesia is affected by fluctuations in foreign exchange rates, especially the US Dollar. Determination of foreign exchange rates must be profitable so a country can run its economy well. The prediction of the exchange rate is done to find out the large exchange rates that occur in the future and the government can take the right policy. Prediction is done by one of the Machine Learning methods, namely the Support Vector Regression (SVR) algorithm. The prediction model is made using three kernels in SVR. Each kernel has the best model and, the accuracy and error value
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Chinta, Srujan Sai. "Kernelised Rough Sets Based Clustering Algorithms Fused With Firefly Algorithm for Image Segmentation." International Journal of Fuzzy System Applications 8, no. 4 (2019): 25–38. http://dx.doi.org/10.4018/ijfsa.2019100102.

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Data clustering methods have been used extensively for image segmentation in the past decade. In one of the author's previous works, this paper has established that combining the traditional clustering algorithms with a meta-heuristic like the Firefly Algorithm improves the stability of the output as well as the speed of convergence. It is well known now that the Euclidean distance as a measure of similarity has certain drawbacks and so in this paper we replace it with kernel functions for the study. In fact, the authors combined Rough Fuzzy C-Means (RFCM) and Rough Intuitionistic Fuzzy C-Mean
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Dr., R. Muthukrishnan, and Udaya Prakash N. "Validate Model Endorsed for Support Vector Machine Alignment with Kernel Function and Depth Concept to Get Superlative Accurateness." International Journal of Basic Sciences and Applied Computing (IJBSAC) 9, no. 7 (2023): 1–5. https://doi.org/10.35940/ijbsac.G0486.039723.

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<strong>Abstract: </strong>A support vector machine (SVM) is authoritative tool for statistical learning model which is well proved based on the literature reviews which is rooted in finding the operational risk. The Key factor is kernel function and its parameters selection. Once the debate of finalizing the influence factor (i.e) kernel parameters and error penalty factors, we can able to find the new kernel function as a proposed model by bring together the kernel with robust depth procedures. Here the GSOsvm has turn out to be best kernel function with local features to a global representa
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Rati Assyifa Putri, Bahriddin Abapihi, and Dian Christien Arisona. "Support Vector Machine: Classification of Trade Balance of Provincies in Indonesia Based on Gross Regional Domestic Product and Large Trade Price Index in 2023." International Journal of Economics, Management and Accounting 1, no. 2 (2024): 221–31. http://dx.doi.org/10.61132/ijema.v1i2.68.

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The aim of this research is to classify Indonesia's trade balance data using the SVM (Support Vector Machine) method with two features, namely Gross Regional Domestic Product (X1) and Wholesale Price Index (X2). Classification is carried out by comparing two types of kernels, namely polynomial kernels and RBF (Radial Basis Function) kernels. Equality Hyperplaneobtained from the polynomial kernel is: . The Hyperplane equation obtained from the RBF kernel is: Experimental results show that classification with polynomial kernels provides better performance than RBF kernels. This can be seen in th
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Dissertations / Theses on the topic "Radial basis Kernel"

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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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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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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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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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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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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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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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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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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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"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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Book chapters on the topic "Radial basis Kernel"

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Chen, W., H. Wang, and Q. H. Qin. "Kernel Radial Basis Functions." In Computational Mechanics. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-75999-7_147.

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Alsalamah, Mashail, and Saad Amin. "Multilayer Radial Basis Function Kernel Machine." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58877-3_34.

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Chudzian, Paweł. "Radial Basis Function Kernel Optimization for Pattern Classification." In Computer Recognition Systems 4. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20320-6_11.

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Lazzaretti, André E., and David M. J. Tax. "An Adaptive Radial Basis Function Kernel for Support Vector Data Description." In Similarity-Based Pattern Recognition. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24261-3_9.

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Kumar, Dash Ch Sanjeev, Pandia Manoj Kumar, Dehuri Satchidananda, and Cho Sung-Bae. "Mixture Kernel Radial Basis Functions Neural Networks for Web Log Classification." In Advances in Intelligent Systems and Computing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35314-7_1.

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Georgoulis, E. H., J. Levesley, and F. Subhan. "Multilevel Sparse Kernel-Based Interpolation Using Conditionally Positive Definite Radial Basis Functions." In Numerical Mathematics and Advanced Applications 2011. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33134-3_17.

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Fasshauer, Gregory E. "Green’s Functions: Taking Another Look at Kernel Approximation, Radial Basis Functions, and Splines." In Springer Proceedings in Mathematics. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0772-0_4.

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Mansor, Nur Suhaili, Hapini Awang, Sarkin Tudu Shehu Malami, Amirulikhsan Zolkafli, Mohammed Ahmed Taiye, and Hanhan Maulana. "Support Vector Machine for Satellite Images Classification Using Radial Basis Function Kernel Method." In Communications in Computer and Information Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9589-9_23.

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Olivieri, Alejandro C. "Non-linearity and Artificial Neural Networks. Radial Basis Functions and Kernel Partial Least-Squares." In Introduction to Multivariate Calibration. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-64144-2_13.

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Candia-García, Cristian, Manuel G. Forero, and Sergio Herrera-Rivera. "Generating Random Variates via Kernel Density Estimation and Radial Basis Function Based Neural Networks." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13469-3_29.

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Conference papers on the topic "Radial basis Kernel"

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Xiaonan, LIU, and Zhang Hengjing. "Analysis of DEM interpolation accuracy based on radial basis kernel function in different topographic conditions." In Sixth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2024), edited by Zhiliang Qin, Jun Chen, and Huaichun Wu. SPIE, 2025. https://doi.org/10.1117/12.3057539.

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H, Muralidhar, Haider M. Abbas, K. Rajeshkumar, and E. Theerthamali. "Evaluation of Compressive Strength of Self-Consolidating Concrete Using Radial Basis Function Canberra Kernel Based Support Vector Machine Model." In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS). IEEE, 2025. https://doi.org/10.1109/icicacs65178.2025.10967945.

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Sathishkumar, S., and P. Parameswari. "Optimizing Early Lung Cancer Detection with Divide and Conquer Kernel SVM and Radial Basis Function Neural Network enchanced by SMOTE." In 2024 Second International Conference Computational and Characterization Techniques in Engineering & Sciences (IC3TES). IEEE, 2024. https://doi.org/10.1109/ic3tes62412.2024.10877434.

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Natanael, Kevin, Richard Vincentius, and Dyah Erny Herwindiati. "Comparison of Linear and Radial Basis Function Kernels in Support Vector Machine for Rice Variety Classification." In 2025 IEEE 15th Symposium on Computer Applications & Industrial Electronics (ISCAIE). IEEE, 2025. https://doi.org/10.1109/iscaie64985.2025.11081051.

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Arif, Omar, and Patricio Antonio Vela. "Kernel map compression using generalized radial basis functions." In 2009 IEEE 12th International Conference on Computer Vision (ICCV). IEEE, 2009. http://dx.doi.org/10.1109/iccv.2009.5459351.

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Yeh, I.-Cheng, Xin-Ying Zhang, Chong Wu, and Kuan-Chieh Huang. "Radial basis function networks with adjustable kernel shape parameters." In 2010 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.5580841.

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Jin, Zheming, and Hal Finkel. "Optimizing Radial Basis Function Kernel on OpenCL FPGA Platform." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622219.

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Ashir, Abubakar M., and Bayram Akdemir. "Facial expression recognition with an optimized radial basis kernel." In 2018 6th International Symposium on Digital Forensic and Security (ISDFS). IEEE, 2018. http://dx.doi.org/10.1109/isdfs.2018.8355366.

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Jin, Zheming, and Hal Finkel. "Evaluating Radial Basis Function Kernel on OpenCL FPGA Platform." In 2018 Ninth International Green and Sustainable Computing Conference (IGSC). IEEE, 2018. http://dx.doi.org/10.1109/igcc.2018.8752172.

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Barsic, David, Craig Carmen, Carlos Renjifo, Kevin Norman, and G. Peacock. "Building Efficient Radial Basis Function Kernel Classifiers using Iterative Methods." In 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing. IEEE, 2006. http://dx.doi.org/10.1109/mlsp.2006.275512.

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