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Journal articles on the topic 'Radial basis Kernel'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

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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10

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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11

Kaminski, W., and P. Strumillo. "Kernel orthonormalization in radial basis function neural networks." IEEE Transactions on Neural Networks 8, no. 5 (1997): 1177–83. http://dx.doi.org/10.1109/72.623218.

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12

Looney, Carl G. "Fuzzy connectivity clustering with radial basis kernel functions." Fuzzy Sets and Systems 160, no. 13 (2009): 1868–85. http://dx.doi.org/10.1016/j.fss.2008.12.010.

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13

Chen, J. S., W. Hu, and H. Y. Hu. "Reproducing kernel enhanced local radial basis collocation method." International Journal for Numerical Methods in Engineering 75, no. 5 (2008): 600–627. http://dx.doi.org/10.1002/nme.2269.

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Dash, Ch Sanjeev Kumar, Ajit Kumar Behera, Satchidananda Dehuri, and Sung-Bae Cho. "A Novel Radial Basis Function Networks Locally Tuned with Differential Evolution for Classification." International Journal of Systems Biology and Biomedical Technologies 2, no. 2 (2013): 33–57. http://dx.doi.org/10.4018/ijsbbt.2013040103.

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The classification of diseases appears as one of the fundamental problems for a medical practitioner, which might be substantially improved by intelligent systems. The present work is aimed at designing in what way an intelligent system supporting medical decision can be developed by hybridizing radial basis function neural networks (RBFNs) and differential evolution (DE). To this extent, a two phases learning algorithm with a modified kernel for radial basis function neural networks is proposed for classification. In phase one, differential evolution is used to reveal the parameters of the mo

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Min, Beomjun, Jongin Kim, Hyeong-jun Park, and Boreom Lee. "Vowel Imagery Decoding toward Silent Speech BCI Using Extreme Learning Machine with Electroencephalogram." BioMed Research International 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/2618265.

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The purpose of this study is to classify EEG data on imagined speech in a single trial. We recorded EEG data while five subjects imagined different vowels, /a/, /e/, /i/, /o/, and /u/. We divided each single trial dataset into thirty segments and extracted features (mean, variance, standard deviation, and skewness) from all segments. To reduce the dimension of the feature vector, we applied a feature selection algorithm based on the sparse regression model. These features were classified using a support vector machine with a radial basis function kernel, an extreme learning machine, and two va

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Dash, Ch Sanjeev Kumar, Ajit Kumar Behera, Satchidananda Dehuri, and Sung-Bae Cho. "Differential Evolution-Based Optimization of Kernel Parameters in Radial Basis Function Networks for Classification." International Journal of Applied Evolutionary Computation 4, no. 1 (2013): 56–80. http://dx.doi.org/10.4018/jaec.2013010104.

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In this paper a two phases learning algorithm with a modified kernel for radial basis function neural networks is proposed for classification. In phase one a new meta-heuristic approach differential evolution is used to reveal the parameters of the modified kernel. The second phase focuses on optimization of weights for learning the networks. Further, a predefined set of basis functions is taken for empirical analysis of which basis function is better for which kind of domain. The simulation result shows that the proposed learning mechanism is evidently producing better classification accuracy

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17

Ying, Yiming, and Colin Campbell. "Rademacher Chaos Complexities for Learning the Kernel Problem." Neural Computation 22, no. 11 (2010): 2858–86. http://dx.doi.org/10.1162/neco_a_00028.

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We develop a novel generalization bound for learning the kernel problem. First, we show that the generalization analysis of the kernel learning problem reduces to investigation of the suprema of the Rademacher chaos process of order 2 over candidate kernels, which we refer to as Rademacher chaos complexity. Next, we show how to estimate the empirical Rademacher chaos complexity by well-established metric entropy integrals and pseudo-dimension of the set of candidate kernels. Our new methodology mainly depends on the principal theory of U-processes and entropy integrals. Finally, we establish s

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18

Huang, Fenghua, and Luming Yan. "Combined Kernel-Based BDT-SMO Classification of Hyperspectral Fused Images." Scientific World Journal 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/738250.

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To solve the poor generalization and flexibility problems that single kernel SVM classifiers have while classifying combined spectral and spatial features, this paper proposed a solution to improve the classification accuracy and efficiency of hyperspectral fused images: (1) different radial basis kernel functions (RBFs) are employed for spectral and textural features, and a new combined radial basis kernel function (CRBF) is proposed by combining them in a weighted manner; (2) the binary decision tree-based multiclass SMO (BDT-SMO) is used in the classification of hyperspectral fused images;

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Dash, Ch Sanjeev Kumar, Pulak Sahoo, Satchidananda Dehuri, and Sung-Bae Cho. "An Empirical Analysis of Evolved Radial Basis Function Networks and Support Vector Machines with Mixture of Kernels." International Journal on Artificial Intelligence Tools 24, no. 04 (2015): 1550013. http://dx.doi.org/10.1142/s021821301550013x.

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Classification is one of the most fundamental and formidable tasks in many domains including biomedical. In biomedical domain, the distributions of data in most of the datasets into predefined number of classes is significantly different (i.e., the classes are distributed unevenly). Many mathematical, statistical, and machine learning approaches have been developed for classification of biomedical datasets with a varying degree of success. This paper attempts to analyze the empirical performance of two forefront machine learning algorithms particularly designed for classification problem by ad

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20

Ding, Xiaojian, Jian Liu, Fan Yang, and Jie Cao. "Random radial basis function kernel-based support vector machine." Journal of the Franklin Institute 358, no. 18 (2021): 10121–40. http://dx.doi.org/10.1016/j.jfranklin.2021.10.005.

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21

Yeh, I.-Cheng, Chung-Chih Chen, Xinying Zhang, Chong Wu, and Kuan-Chieh Huang. "Adaptive radial basis function networks with kernel shape parameters." Neural Computing and Applications 21, no. 3 (2010): 469–80. http://dx.doi.org/10.1007/s00521-010-0485-2.

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22

Zhang, Ting, Gaofeng Wei, Jichao Ma, and Hongfen Gao. "Radial basis reproducing kernel particle method for piezoelectric materials." Engineering Analysis with Boundary Elements 92 (July 2018): 171–79. http://dx.doi.org/10.1016/j.enganabound.2017.10.020.

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23

Zeng, Jie, Panayiotis C. Roussis, Ahmed Salih Mohammed, et al. "Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels." Applied Sciences 11, no. 8 (2021): 3705. http://dx.doi.org/10.3390/app11083705.

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This research examines the feasibility of hybridizing boosted Chi-Squared Automatic Interaction Detection (CHAID) with different kernels of support vector machine (SVM) techniques for the prediction of the peak particle velocity (PPV) induced by quarry blasting. To achieve this objective, a boosting-CHAID technique was applied to a big experimental database comprising six input variables. The technique identified four input parameters (distance from blast-face, stemming length, powder factor, and maximum charge per delay) as the most significant parameters affecting the prediction accuracy and

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Yang, Judy P., and Yuan-Chia Chen. "Gradient Enhanced Localized Radial Basis Collocation Method for Inverse Analysis of Cauchy Problems." International Journal of Applied Mechanics 12, no. 09 (2020): 2050107. http://dx.doi.org/10.1142/s1758825120501070.

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This work proposes a gradient enhanced localized radial basis collocation method (GL-RBCM) for solving boundary value problems. In particular, the attention is paid to the solution of inverse Cauchy problems. It is known that the approximation by radial basis functions often leads to ill-conditioned systems due to the global nature. To this end, the reproducing kernel shape function and gradient reproducing kernel shape function are proposed to localize the radial basis function while the gradient approximation is aimed at reducing the computational intensity of carrying out the second derivat

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25

Samsudin, Nor Ain Maisarah, Shazlyn Milleana Shaharudin, Nurul Ainina Filza Sulaiman, Shuhaida I. smail, Nur Syarafina Mohamed, and Nor Hafizah Md Husin. "Prediction of Student‘s Academic Performance during Online Learning Based on Regression in Support Vector Machine." International Journal of Information and Education Technology 12, no. 12 (2022): 1431–35. http://dx.doi.org/10.18178/ijiet.2022.12.12.1768.

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Since the Movement Control Order (MCO) was adopted, all the universities have implemented and modified the principle of online learning and teaching in consequence of Covid-19. This situation has relatively affected the students’ academic performance. Therefore, this paper employs the regression method in Support Vector Machine (SVM) to investigate the prediction of students’ academic performance in online learning during the Covid-19 pandemic. The data was collected from undergraduate students of the Department of Mathematics, Faculty of Science and Mathematics, Sultan Idris Education Univers

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Glori, Stephani Saragih, Hartini Sri, and Rustam Zuherman. "Comparison between fuzzy kernel k-medoids using radial basis function kernel and polynomial kernel function in hepatitis classification." International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (2021): 60–65. https://doi.org/10.11591/ijai.v10.i1.pp60-65.

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This paper compares the fuzzy kernel k-medoids using radial basis function (RBF) and polynomial kernel function in hepatitis classification. These two kernel functions were chosen due to their popularity in any kernel-based machine learning method for solving the classification task. The hepatitis dataset then used to evaluate the performance of both methods that were expected to provide an accurate diagnosis in patients to obtain treatment at an early phase. The data were obtained from two hospitals in Indonesia, consisting of 89 hepatitis-B and 31 hepatitis-C samples. The data were analyzed

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Pan, Shuang, Jianguo Wei, and Hao Pan. "Study on Evaluation Model of Chinese P2P Online Lending Platform Based on Hybrid Kernel Support Vector Machine." Scientific Programming 2020 (May 8, 2020): 1–7. http://dx.doi.org/10.1155/2020/4561834.

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Accurate evaluation of the risk level and operation performances of P2P online lending platforms is not only conducive to better functioning of information intermediaries but also effective protection of investors’ interests. This paper proposes a genetic algorithm (GA) improved hybrid kernel support vector machine (SVM) with an index system to construct such an evaluation model. A hybrid kernel consisting of polynomial function and radial basis function is improved, specifically kernel parameters and the weight of two kernels, by GA method with excellent global optimization and rapid converge

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SRIVASTAVA, ANKUR, and ANDREW J. MEADE. "A SPARSE GREEDY SELF-ADAPTIVE ALGORITHM FOR CLASSIFICATION OF DATA." Advances in Adaptive Data Analysis 02, no. 01 (2010): 97–114. http://dx.doi.org/10.1142/s1793536910000355.

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Kernels have become an integral part of most data classification algorithms. However, the kernel parameters are generally not optimized during learning. In this work a novel adaptive technique called Sequential Function Approximation (SFA) has been developed for classification that determines the values of the control and kernel hyper-parameters during learning. This tool constructs sparse radial basis function networks in a greedy fashion. Experiments were carried out on synthetic and real-world data sets where SFA had comparable performance to other popular classification schemes with parame

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Chen, Yifei, Feng Liu, Bram Vanschoenwinkel, and Bernard Manderick. "Splice Site Prediction using Support Vector Machines with Context-Sensitive Kernel Functions." JUCS - Journal of Universal Computer Science 15, no. (13) (2009): 2528–46. https://doi.org/10.3217/jucs-015-13-2528.

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This paper focuses on the use of support vector machines on a typical context-dependent classification task, splice site prediction. For this type of problems, it has been shown that a context-based approach should be preferred over a transformation approach because the former approach can easily incorporate statistical measures or directly plug sensitivity information into distance functions. In this paper, we designed three types of context-sensitive kernel functions: polynomial-based, radial basis function-based and negative distance-based kernels. From the experimental results it becomes c

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Tan, Ruomu, James R. Ottewill, and Nina F. Thornhill. "Monitoring Statistics and Tuning of Kernel Principal Component Analysis With Radial Basis Function Kernels." IEEE Access 8 (2020): 198328–42. http://dx.doi.org/10.1109/access.2020.3034550.

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Klepáč, Václav, and David Hampel. "Prediction of Bankruptcy with SVM Classifiers Among Retail Business Companies in EU." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 64, no. 2 (2016): 627–34. http://dx.doi.org/10.11118/actaun201664020627.

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Article focuses on the prediction of bankruptcy of the 850 medium-sized retail business companies in EU from which 48 companies gone bankrupt in 2014 with respect to lag of the used features. From various types of classification models we chose Support vector machines method with linear, polynomial and radial kernels to acquire best results. Pre-processing is enhanced with filter based feature selection like Gain ratio, Chi-square and Relief algorithm to acquire attributes with the best information value. On this basis we deal with random samples of financial data to measure prediction accurac

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32

Kokate, Shrikant, and Manna Sheela Rani Chettyi. "Fraudulent event detection in credit card data operations using SVM-RBF kernel function machine learning classification technique." Intelligent Decision Technologies 19, no. 2 (2025): 660–69. https://doi.org/10.1177/18724981241305118.

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Credit card operations are essential to human life as technological development has been overgrown in the last few years. Customers get connected with online operation systems easily due to the ease of the process of online systems. As technology grows rapidly the fraud associated with credit operations also grows rapidly along with technological developments. To find or identify such kinds of fraud actions, machine learning, and its classifier techniques play a very important part in identifying the patterns of fraud and legitimate actions of customers based on earlier operations. To identify

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Ancona, Nicola, and Sebastiano Stramaglia. "An Invariance Property of Predictors in Kernel-Induced Hypothesis Spaces." Neural Computation 18, no. 4 (2006): 749–59. http://dx.doi.org/10.1162/neco.2006.18.4.749.

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We consider kernel-based learning methods for regression and analyze what happens to the risk minimizer when new variables, statistically independent of input and target variables, are added to the set of input variables. This problem arises, for example, in the detection of causality relations between two time series. We find that the risk minimizer remains unchanged if we constrain the risk minimization to hypothesis spaces induced by suitable kernel functions. We show that not all kernel-induced hypothesis spaces enjoy this property. We present sufficient conditions ensuring that the risk m

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Latif, Shereen Hamdy Abdel, Asraa Sadoon Alwan, and Amany Mousa Mohamed. "Principal component analysis as tool for data reduction with an application." EUREKA: Physics and Engineering, no. 5 (September 30, 2022): 184–98. http://dx.doi.org/10.21303/2461-4262.2022.002577.

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The recent trends in collecting huge datasets have posed a great challenge that is brought by the high dimensionality and aggravated by the presence of irrelevant dimensions. Machine learning models for regression is recognized as a convenient way of improving the estimation for empirical models. Popular machine learning models is support vector regression (SVR). However, the usage of principal component analysis (PCA) as a variable reduction method along with SVR is suggested. The principal component analysis helps in building a predictive model that is simple as it contains the smallest numb

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Latif, Shereen Hamdy Abdel, Asraa Sadoon Alwan, and Amany Mousa Mohamed. "Principal component analysis as tool for data reduction with an application." EUREKA: Physics and Engineering, no. 5 (September 30, 2022): 184–98. https://doi.org/10.21303/2461-4262.2022.002577.

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The recent trends in collecting huge datasets have posed a great challenge that is brought by the high dimensionality and aggravated by the presence of irrelevant dimensions. Machine learning models for regression is recognized as a convenient way of improving the estimation for empirical models. Popular machine learning models is support vector regression (SVR). However, the usage of principal component analysis (PCA) as a variable reduction method along with SVR is suggested. The principal component analysis helps in building a predictive model that is simple as it contains the smallest numb

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Liu, Zheng, Gaofeng Wei, Zhiming Wang, and Jinwei Qiao. "The Meshfree Analysis of Geometrically Nonlinear Problem Based on Radial Basis Reproducing Kernel Particle Method." International Journal of Applied Mechanics 12, no. 04 (2020): 2050044. http://dx.doi.org/10.1142/s1758825120500441.

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Based on the reproducing kernel particle method (RKPM) and the radial basis function (RBF), the radial basis reproducing kernel particle method (RRKPM) is presented for solving geometrically nonlinear problems. The advantages of the presented method are that it can eliminate the negative effect of diverse kernel functions on the computational accuracy and has greater computational accuracy and better convergence than the RKPM. Using the weak form of Galerkin integration and the Total Lagrangian (T.L.) formulation, the correlation formulae of the RRKPM for geometrically nonlinear problem are ob

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Alpay, Daniel, Fabrizio Colombo, Kamal Diki, and Irene Sabadini. "An approach to the Gaussian RBF kernels via Fock spaces." Journal of Mathematical Physics 63, no. 11 (2022): 113506. http://dx.doi.org/10.1063/5.0060342.

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We use methods from the Fock space and Segal–Bargmann theories to prove several results on the Gaussian RBF kernel in complex analysis. The latter is one of the most used kernels in modern machine learning kernel methods and in support vector machine classification algorithms. Complex analysis techniques allow us to consider several notions linked to the radial basis function (RBF) kernels, such as the feature space and the feature map, using the so-called Segal–Bargmann transform. We also show how the RBF kernels can be related to some of the most used operators in quantum mechanics and time

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Bodyanskiy, Evgenij V., Anastasija A. Deineko, and Jana V. Kutsenko. "KERNEL SELF-ORGANIZING MAP BASED ON RADIAL-BASIS NEURAL NETWORK." ELECTRICAL AND COMPUTER SYSTEMS 20, no. 96 (2015): 97–105. http://dx.doi.org/10.15276/eltecs.20.96.2015.13.

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Ying, Lexing. "A kernel independent fast multipole algorithm for radial basis functions." Journal of Computational Physics 213, no. 2 (2006): 451–57. http://dx.doi.org/10.1016/j.jcp.2005.09.010.

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Ma, Limin, and Zongmin Wu. "Kernel based approximation in Sobolev spaces with radial basis functions." Applied Mathematics and Computation 215, no. 6 (2009): 2229–37. http://dx.doi.org/10.1016/j.amc.2009.08.012.

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Wang, Ke, Ligang Cheng, and Bin Yong. "Spectral-Similarity-Based Kernel of SVM for Hyperspectral Image Classification." Remote Sensing 12, no. 13 (2020): 2154. http://dx.doi.org/10.3390/rs12132154.

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Spectral similarity measures can be regarded as potential metrics for kernel functions, and can be used to generate spectral-similarity-based kernels. However, spectral-similarity-based kernels have not received significant attention from researchers. In this paper, we propose two novel spectral-similarity-based kernels based on spectral angle mapper (SAM) and spectral information divergence (SID) combined with the radial basis function (RBF) kernel: Power spectral angle mapper RBF (Power-SAM-RBF) and normalized spectral information divergence-based RBF (Normalized-SID-RBF) kernels. First, we

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Dash, Ch Sanjeev Kumar, Ajit Kumar Behera, Satchidananda Dehuri, and Sung-Bae Cho. "Radial basis function neural networks: a topical state-of-the-art survey." Open Computer Science 6, no. 1 (2016): 33–63. http://dx.doi.org/10.1515/comp-2016-0005.

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AbstractRadial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have shown good performance in a variety of application domains. They have potential for hybridization and demonstrate some interesting emergent behaviors. This paper aims to offer a compendious and sensible survey on RBF networks. The advantages they offer, such as fast training and global approximation capability with local responses, are attracting many researchers to use them in diversified fields. The overall algorithmic development of RBF networks by giving special focus on their learning

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Zaki, Taher, Driss Mammass, Abdellatif Ennaji, and Stéphane Nicolas. "Arabic Documents Classification by a Radial Basis Hybridization." International Journal of Mathematical Models and Methods in Applied Sciences 15 (November 23, 2021): 140–47. http://dx.doi.org/10.46300/9101.2021.15.18.

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In this paper, we propose a hybrid system for contextual and semantic indexing of Arabic documents, bringing an improvement to classical models based on n-grams and the Okapi model. This new approach takes into account the concept of the semantic vicinity of terms. We proceed in fact by the calculation of similarity between words using an hybridization of NGRAMs-OKAPI statistical measures and a kernel function in order to identify relevant descriptors. Terminological resources such as graphs and semantic dictionaries are integrated into the system to improve the indexing and the classification

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Park, J., and I. W. Sandberg. "Universal Approximation Using Radial-Basis-Function Networks." Neural Computation 3, no. 2 (1991): 246–57. http://dx.doi.org/10.1162/neco.1991.3.2.246.

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There have been several recent studies concerning feedforward networks and the problem of approximating arbitrary functionals of a finite number of real variables. Some of these studies deal with cases in which the hidden-layer nonlinearity is not a sigmoid. This was motivated by successful applications of feedforward networks with nonsigmoidal hidden-layer units. This paper reports on a related study of radial-basis-function (RBF) networks, and it is proved that RBF networks having one hidden layer are capable of universal approximation. Here the emphasis is on the case of typical RBF network

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Gerstoft, Peter, Manual Hahmann, William F. Jenkins, Zoi-Heleni Michalopoulou, Efren Fernandez-Grande, and Christoph Mecklenbrauker. "Direction of arrival estimation using Gaussian process interpolation." Journal of the Acoustical Society of America 152, no. 4 (2022): A142. http://dx.doi.org/10.1121/10.0015829.

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Gaussian processes (GP) have been used to predict acoustic fields by interpolating under-sampled field observations. Using GP interpolation to predict fields is advantageous due to its ability to denoise measurements, and for its prediction of likely field outcomes given a certain field coherence, or in GP terminology, a kernel. While there are many design options for a coherence function, in this study we examine using the radial basis function kernel, the physically based plane wave kernel, and a composition of plane wave kernels representing a certain angular interval of directions. The com

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Azmi, Fadhillah, and Amir Saleh. "A Hybrid Algorithm for Multiple Disease Prediction: Radial Basis Function and Logistic Regression." International Journal of Science and Healthcare Research 9, no. 2 (2024): 363–68. http://dx.doi.org/10.52403/ijshr.20240246.

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Disease prediction is an important aspect of modern medicine, which aims to diagnose disease early and provide appropriate treatment to patients. This research uses a hybrid approach that combines the RBF (Radial Basis Function) kernel algorithm with logistic regression to predict various diseases in medical datasets. This method is intended to improve prediction performance by exploiting the advantages of each algorithm. This research uses a dataset containing medical information about several diseases collected from the Kaggle dataset. First, the RBF kernel is applied to transform the data f

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Stephani Saragih, Glori, Sri Hartini, and Zuherman Rustam. "Comparison between fuzzy kernel k-medoids using radial basis function kernel and polynomial kernel function in hepatitis classification." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (2021): 60. http://dx.doi.org/10.11591/ijai.v10.i1.pp60-65.

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<span id="docs-internal-guid-10508d4e-7fff-5011-7a0e-441840e858c8"><span>This paper compares the fuzzy kernel k-medoids using radial basis function (RBF) and polynomial kernel function in hepatitis classification. These two kernel functions were chosen due to their popularity in any kernel-based machine learning method for solving the classification task. The hepatitis dataset then used to evaluate the performance of both methods that were expected to provide an accurate diagnosis in patients to obtain treatment at an early phase. The data were obtained from two hospitals in Indone

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Kanchana, M., and P. Varalakshmi. "Computer aided system for breast cancer in digitized mammogram using shearlet band features with LS-SVM classifier." International Journal of Wavelets, Multiresolution and Information Processing 14, no. 03 (2016): 1650017. http://dx.doi.org/10.1142/s021969131650017x.

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Breast cancer is life threatening and dangerous diseases among the women across the world. In this paper, mammogram image classification performed using LS-SVM with various kernels functions namely, Gaussian Radial Basis Function (GRBF) kernel, Polynomial kernel, Quadratic kernel, Linear kernel and MLP kernel. Shearlet transform is a multidimensional version of the composite dilation wavelet transform, and is especially designed to address anisotropic and directional information at various scales and directions, which is used to decompose the regions of interest (ROI) image after preprocessing

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Ordiyasa, I. Wayan, Mohammad Diqi, Elisabeth Deta Lustiyati, Marselina Endah Hiswati, and Marcella Salsabela. "Smart Fire Safety: Analyzing Radial Basis Function Kernel in SVM for IoT-driven Smoke Detection." semanTIK 10, no. 1 (2024): 159. http://dx.doi.org/10.55679/semantik.v10i1.47433.

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This research explores the application of Support Vector Machine (SVM) with the Radial Basis Function (RBF) kernel in smoke detection using a dataset collected from Internet of Things (IoT) devices, specifically Photoelectric Smoke Detectors. With 62,630 records and 16 attributes, the study aims to address limitations in smoke detection technology that may impact system accuracy. Through RBF kernel analysis, the SVM model demonstrates the capability to recognize complex patterns related to smoke presence, achieving an accuracy rate of 96.85%. The Classification Report reveals high precision, r

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Indra Septiawati, Eka Suryani, Elvia Budianita, Fitri Insani, and Lola Oktavia. "Prediksi Jumlah Perceraian Menggunakan Metode Support Vector Regression (SVR)." Journal of Computer System and Informatics (JoSYC) 5, no. 1 (2023): 208–17. http://dx.doi.org/10.47065/josyc.v5i1.4613.

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The increasing number of divorces poses an increasingly significant social challenge in Indonesia, including in the city of Pekanbaru. The impact of these divorces on the adolescent population can have negative effects on their emotional and psychological well-being, as well as their ability to interact socially and engage in the learning process. This study utilizes monthly divorce data from 2015 to April 2023 to conduct time series analysis and applies the Support Vector Regression (SVR) method to predict the number of divorces in the city of Pekanbaru. Three types of SVR kernels, namely lin

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