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Journal articles on the topic 'Kernel-based model'

Author: Grafiati

Published: 14 March 2023

Last updated: 31 July 2025

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1

Nishiyama, Yu, Motonobu Kanagawa, Arthur Gretton, and Kenji Fukumizu. "Model-based kernel sum rule: kernel Bayesian inference with probabilistic models." Machine Learning 109, no. 5 (2020): 939–72. http://dx.doi.org/10.1007/s10994-019-05852-9.

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AbstractKernel Bayesian inference is a principled approach to nonparametric inference in probabilistic graphical models, where probabilistic relationships between variables are learned from data in a nonparametric manner. Various algorithms of kernel Bayesian inference have been developed by combining kernelized basic probabilistic operations such as the kernel sum rule and kernel Bayes’ rule. However, the current framework is fully nonparametric, and it does not allow a user to flexibly combine nonparametric and model-based inferences. This is inefficient when there are good probabilistic mod

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Zong, Xinlu, Chunzhi Wang, and Hui Xu. "Density-based Adaptive Wavelet Kernel SVM Model for P2P Traffic Classification." International Journal of Future Generation Communication and Networking 6, no. 6 (2013): 25–36. http://dx.doi.org/10.14257/ijfgcn.2013.6.6.04.

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Shim, Jooyong, and Changha Hwang. "Kernel-based orthogonal quantile regression model." Model Assisted Statistics and Applications 12, no. 3 (2017): 217–26. http://dx.doi.org/10.3233/mas-170396.

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Su, Zhi-gang, Pei-hong Wang, and Zhao-long Song. "Kernel based nonlinear fuzzy regression model." Engineering Applications of Artificial Intelligence 26, no. 2 (2013): 724–38. http://dx.doi.org/10.1016/j.engappai.2012.05.009.

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5

Fan, Yanqin, and Qi Li. "CONSISTENT MODEL SPECIFICATION TESTS." Econometric Theory 16, no. 6 (2000): 1016–41. http://dx.doi.org/10.1017/s0266466600166083.

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We point out the close relationship between the integrated conditional moment tests in Bierens (1982, Journal of Econometrics 20, 105–134) and Bierens and Ploberger (1997, Econometrica 65, 1129–1152) with the complex-valued exponential weight function and the kernel-based tests in Härdle and Mammen (1993, Annals of Statistics 21, 1926–1947), Li and Wang (1998, Journal of Econometrics 87, 145–165), and Zheng (1996, Journal of Econometrics 75, 263–289). It is well established that the integrated conditional moment tests of Bierens (1982) and Bierens and Ploberger (1997) are more powerful than ke

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Marcella, Peter, Minoi Jacey-Lynn, and Ab Rahman Suriani. "Neutral expression synthesis using kernel active shape model." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (2022): 150–57. https://doi.org/10.11591/ijeecs.v20.i1.pp150-157.

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This paper presents a modified kernel-based Active Shape Model for neutralizing and synthesizing facial expressions. In recent decades, facial identity and emotional studies have gained interest from researchers, especially in the works of integrating human emotions and machine learning to improve the current lifestyle. It is known that facial expressions are often associated with face recognition systems with poor recognition rate. In this research, a method of a modified kernel-based active shape model based on statistical-based approach is introduced to synthesize neutral (neutralize) expre

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Wang, Zhijie, Mohamed Ben Salah, Hong Zhang, and Nilanjan Ray. "Shape based appearance model for kernel tracking." Image and Vision Computing 30, no. 4-5 (2012): 332–44. http://dx.doi.org/10.1016/j.imavis.2012.03.003.

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Ma, Xin, and Zhi-bin Liu. "The kernel-based nonlinear multivariate grey model." Applied Mathematical Modelling 56 (April 2018): 217–38. http://dx.doi.org/10.1016/j.apm.2017.12.010.

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Lingyu, Liang, Wenqi Huang, Zhaojie Dong, et al. "Short-term power load forecasting based on combined kernel Gaussian process hybrid model." E3S Web of Conferences 256 (2021): 01009. http://dx.doi.org/10.1051/e3sconf/202125601009.

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As one of the countries with the most energy consumption in the world, electricity accounts for a large proportion of the energy supply in our country. According to the national basic policy of energy conservation and emission reduction, it is urgent to realize the intelligent distribution and management of electricity by prediction. Due to the complex nature of electricity load sequences, the traditional model predicts poor results. As a kernel-based machine learning model, Gaussian Process Mixing (GPM) has high predictive accuracy, can multi-modal prediction and output confidence intervals.

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Qian, Yuqing, Tingting Shang, Fei Guo, et al. "Identification of DNA-binding protein based multiple kernel model." Mathematical Biosciences and Engineering 20, no. 7 (2023): 13149–70. http://dx.doi.org/10.3934/mbe.2023586.

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<abstract> <p>DNA-binding proteins (DBPs) play a critical role in the development of drugs for treating genetic diseases and in DNA biology research. It is essential for predicting DNA-binding proteins more accurately and efficiently. In this paper, a Laplacian Local Kernel Alignment-based Restricted Kernel Machine (LapLKA-RKM) is proposed to predict DBPs. In detail, we first extract features from the protein sequence using six methods. Second, the Radial Basis Function (RBF) kernel function is utilized to construct pre-defined kernel metrics. Then, these metrics are combined linea

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Qi, Jinshan, Xun Liang, and Rui Xu. "A Multiple Kernel Learning Model Based on p-Norm." Computational Intelligence and Neuroscience 2018 (2018): 1–7. http://dx.doi.org/10.1155/2018/1018789.

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By utilizing kernel functions, support vector machines (SVMs) successfully solve the linearly inseparable problems. Subsequently, its applicable areas have been greatly extended. Using multiple kernels (MKs) to improve the SVM classification accuracy has been a hot topic in the SVM research society for several years. However, most MK learning (MKL) methods employ L1-norm constraint on the kernel combination weights, which forms a sparse yet nonsmooth solution for the kernel weights. Alternatively, the Lp-norm constraint on the kernel weights keeps all information in the base kernels. Nonethele

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Elaissi, Ilyes, Okba Taouali, and Messaoud Hassani. "Online Prediction Model Based on New Kernel Method." International Review of Automatic Control (IREACO) 7, no. 1 (2014): 107. http://dx.doi.org/10.15866/ireaco.v7i1.1299.

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13

Wu, Xiao-hong, and Jian-jiang Zhou. "Modified possibilistic clustering model based on kernel methods." Journal of Shanghai University (English Edition) 12, no. 2 (2008): 136–40. http://dx.doi.org/10.1007/s11741-008-0210-2.

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14

Zhang, Zhihua, James T. Kwok, and Dit-Yan Yeung. "Model-based transductive learning of the kernel matrix." Machine Learning 63, no. 1 (2006): 69–101. http://dx.doi.org/10.1007/s10994-006-6130-8.

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15

Carnerero, Panduro Alfonso Daniel, Ramirez Daniel R., Daniel Limon, and Teodoro Alamo. "Kernel-based State-Space Kriging for Predictive Control." IEEE CAA Journal of Automatica Sinica 10, no. 5 (2023): 1263–75. https://doi.org/10.1109/JAS.2023.123459.

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In this paper, we extend the State-Space Kriging (SSK) modeling technique presented in a previous work by the authors in order to consider non-autonomous systems. SSK is a data-driven method that computes predictions as linear combinations of past outputs. To model the nonlinear dynamics of the system, we propose the Kernel-based State-Space Kriging (K-SSK), a new version of the SSK where kernel functions are used instead of resorting to considerations about the locality of the data. Also, a Kalman filter can be used to improve the predictions at each time step in the case of noisy measurement

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SenGupta, Ishuita, Anil Kumar, and Rakesh Kumar Dwivedi. "Assimilation of Standard Regularizer Contextual Model and Composite Kernel with Fuzzy-based Noise Classifier." Journal of Modeling and Optimization 11, no. 1 (2019): 16–24. http://dx.doi.org/10.32732/jmo.2019.11.1.16.

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The paper assay the effect of assimilating smoothness prior contextual model and composite kernel function with fuzzy based noise classifier using remote sensing data. The concept of the composite kernel has been taken by fusing two kernels together to improve the classification accuracy. Gaussian and Sigmoid kernel functions have opted for kernel composition. As a contextual model, Markov Random Field (MRF) Standard regularization model (smoothness prior) has been studied with the composite kernel-based Noise Classifier. Comparative analysis of new classifier with the conventional construes i

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Li, Jianliang, Xiaohai Li, Robert Lugg, and Lawrence S. Melvin. "Kernel Count Reduction in Model Based Optical Proximity Correction Process Models." Japanese Journal of Applied Physics 48, no. 6 (2009): 06FA05. http://dx.doi.org/10.1143/jjap.48.06fa05.

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18

Nadim, Mohammad, Wonjun Lee, and David Akopian. "Characteristic Features of the Kernel-level Rootkit for Learning-based Detection Model Training." Electronic Imaging 2021, no. 3 (2021): 34–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.3.mobmu-034.

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The core part of the operating system is the kernel, and it plays an important role in managing critical data structure resources for correct operations. The kernel-level rootkits are the most elusive type of malware that can modify the running OS kernel in order to hide its presence and perform many malicious activities such as process hiding, module hiding, network communication hiding, and many more. In the past years, many approaches have been proposed to detect kernel-level rootkit. Still, it is challenging to detect new attacks and properly categorize the kernel-level rootkits. Memory fo

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19

Segera, Davies, Mwangi Mbuthia, and Abraham Nyete. "Particle Swarm Optimized Hybrid Kernel-Based Multiclass Support Vector Machine for Microarray Cancer Data Analysis." BioMed Research International 2019 (December 16, 2019): 1–11. http://dx.doi.org/10.1155/2019/4085725.

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Determining an optimal decision model is an important but difficult combinatorial task in imbalanced microarray-based cancer classification. Though the multiclass support vector machine (MCSVM) has already made an important contribution in this field, its performance solely depends on three aspects: the penalty factor C, the type of kernel, and its parameters. To improve the performance of this classifier in microarray-based cancer analysis, this paper proposes PSO-PCA-LGP-MCSVM model that is based on particle swarm optimization (PSO), principal component analysis (PCA), and multiclass support

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20

Abakar, Khalid AA, and Chongwen Yu. "The Spinning Quality Control Management Based on Decision Making by Data Mining Techniques." International Journal of Emerging Research in Management and Technology 7, no. 1 (2018): 72. http://dx.doi.org/10.23956/ijermt.v7i1.25.

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This work demonstrated the possibility of using the data mining techniques such as artificial neural networks (ANN) and support vector machine (SVM) based model to predict the quality of the spinning yarn parameters. Three different kernel functions were used as SVM kernel functions which are Polynomial and Radial Basis Function (RBF) and Pearson VII Function-based Universal Kernel (PUK) and ANN model were used as data mining techniques to predict yarn properties. In this paper, it was found that the SVM model based on Person VII kernel function (PUK) have the same performance in prediction of

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Zhai, Yuejing, Zhouzheng Li, and Haizhong Liu. "Multi-Angle Fast Neural Tangent Kernel Classifier." Applied Sciences 12, no. 21 (2022): 10876. http://dx.doi.org/10.3390/app122110876.

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Multi-kernel learning methods are essential kernel learning methods. Still, the base kernel functions in most multi-kernel learning methods only with select kernel functions with shallow structures, which are weak for large-scale uneven data. We propose two types of acceleration models from a multidimensional perspective of the data: the neural tangent kernel (NTK)-based multi-kernel learning method is proposed, where the NTK kernel regressor is shown to be equivalent to an infinitely wide neural network predictor, and the NTK with deep structure is used as the base kernel function to enhance

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22

Kumagai, Masahito, Kazuhiko Komatsu, Masayuki Sato, and Hiroaki Kobayashi. "Ising-Based Kernel Clustering." Algorithms 16, no. 4 (2023): 214. http://dx.doi.org/10.3390/a16040214.

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Combinatorial clustering based on the Ising model is drawing attention as a high-quality clustering method. However, conventional Ising-based clustering methods using the Euclidean distance cannot handle irregular data. To overcome this problem, this paper proposes an Ising-based kernel clustering method. The kernel clustering method is designed based on two critical ideas. One is to perform clustering of irregular data by mapping the data onto a high-dimensional feature space by using a kernel trick. The other is the utilization of matrix–matrix calculations in the numerical libraries to acce

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23

Feng, Jun, Jian-Zhou Zhang, and Bin Zhou. "Compact Support FDK Kernel Reconstruction Model Base on Approximate Inverse." Mathematical Problems in Engineering 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/109534.

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A novel CT reconstruction model is proposed, and the reconstruction is completed by this kernel-based method. The reconstruction kernel can be obtained by combining the approximate inverse method with the FDK algorithm. The computation of the kernel is moderate, and the reconstruction results can be improved by introducing the compact support version of the kernel. The efficiency and the accuracy are shown in the numerical experiments.

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KUDLAI, Vladyslav, Nataliia BONDARENKO, and Viktor BONDARENKO. "CONSTRUCTION AND VERIFICATION OF A DIGITAL EQUALIZER MODEL." Herald of Khmelnytskyi National University. Technical sciences 313, no. 5 (2022): 178–84. http://dx.doi.org/10.31891/2307-5732-2022-313-5-178-184.

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An approach to the development of an equalizer by building its mathematical model based on a microcontroller is proposed. All operations, including signal processing and equalizer kernel calculation, are performed by a single microcontroller. Thanks to the created mathematical model of the equalizer, the calculation of the kernel is reduced to multiple uses of relatively simple operations, which saves time and memory of the program. The equalizer provides satisfactory processing quality at a small filter order which is selected as a digital filter with final impulse response (FIR) because of i

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25

Gu, Lch, Zhw Ni, and Zhj Wu. "Study of Predictive Method Based on SVM Optimal Model Selection." Applied Mechanics and Materials 65 (June 2011): 443–46. http://dx.doi.org/10.4028/www.scientific.net/amm.65.443.

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The computation time consuming and poor efficiency of prediction exist in the model selection of traditional SVM. By studing on kernel matrix, a SVM-based prediction method for selecting the optimal model framework SVR-D1.2 was proposed with the help of the kernel matrix’s symmetry and positive definition and kernel alignment. The method was applied to the prediction of wheat scab, and comparison experiments were done with the main existing methods. The result shows the method has more efficiency and precision of prediction in the occurrence tendency of wheat scab. Meanwhile, it is simple, pra

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Sun, Jian Ping, and Lin Tao Hu. "Application of Status Monitoring of Wind Turbines Based on Relevance Vector Machine Regression." Advanced Materials Research 347-353 (October 2011): 2337–41. http://dx.doi.org/10.4028/www.scientific.net/amr.347-353.2337.

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Based on the single kernel function relevance vector machine(RVM) models，a multiple load-forecasting model has been established and simulated with several compound kernel functions, including Gauss kernel, Laplace, linear compounded by Gauss and Laplace, Gauss and polynomial kernel. Each model gained comparatively reasonable results in simulation .Moreover, multi linear-compound kernel RVMs performed better than single kernel RVMs in terms of most evaluating indicators, which prove that RVM is an appropriate machine learning method in monitoring status of components of wind turbines.

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27

Pillonetto, Gianluigi, Tianshi Chen, and Lennart Ljung. "Kernel-based model order selection for linear system identification." IFAC Proceedings Volumes 46, no. 11 (2013): 257–62. http://dx.doi.org/10.3182/20130703-3-fr-4038.00043.

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28

Choklati, A., and K. Sabri. "Cyclic Analysis of Extra Heart Sounds:Gauss Kernel based Model." International Journal of Image, Graphics and Signal Processing 10, no. 5 (2018): 1–14. http://dx.doi.org/10.5815/ijigsp.2018.05.01.

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29

Han, R., Z. Jing, and Y. Li. "Kernel based visual tracking with variant spatial resolution model." Electronics Letters 44, no. 8 (2008): 517. http://dx.doi.org/10.1049/el:20080051.

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30

Gao, F. "Detecting vegetation structure using a kernel-based BRDF model." Remote Sensing of Environment 86, no. 2 (2003): 198–205. http://dx.doi.org/10.1016/s0034-4257(03)00100-7.

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31

Wang, Yong, Xinbin Luo, Lu Ding, Shan Fu, and Shiqiang Hu. "Collaborative model based UAV tracking via local kernel feature." Applied Soft Computing 72 (November 2018): 90–107. http://dx.doi.org/10.1016/j.asoc.2018.07.049.

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32

Yanxiang, Fang, Shen Changxiang, Xu Jingdong, and Wu Gongyi. "A separated domain-based kernel model for trusted computing." Wuhan University Journal of Natural Sciences 11, no. 6 (2006): 1424–28. http://dx.doi.org/10.1007/bf02831789.

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33

Yu, Changyong, Chengtang Yao, Mingtao Pei, and Yunde Jia. "Diffusion-based kernel matrix model for face liveness detection." Image and Vision Computing 89 (September 2019): 88–94. http://dx.doi.org/10.1016/j.imavis.2019.06.009.

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34

Ikeda, Sei ichi, and Yoshiharu Sato. "Kernel methods for regression model based on variable selection." International Journal of Knowledge Engineering and Soft Data Paradigms 1, no. 1 (2009): 49. http://dx.doi.org/10.1504/ijkesdp.2009.021984.

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35

Luo, Dang, and Zhang Huihui. "Grey clustering model based on kernel and information field." Grey Systems: Theory and Application 10, no. 1 (2019): 56–67. http://dx.doi.org/10.1108/gs-08-2019-0029.

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Purpose The purpose of this paper is to propose a grey clustering model based on kernel and information field to deal with the situation in which both the observation values and the turning points of the whitenization weight function are interval grey numbers. Design/methodology/approach First, the “unreduced axiom of degree of greyness” was expanded to obtain the inference of “information field not-reducing”. Then, based on the theoretical basis of inference, the expression of whitenization weight function with interval grey number was provided. The grey clustering model and fuzzy clustering

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36

Suykens, Johan A. K., Carlos Alzate, and Kristiaan Pelckmans. "Primal and dual model representations in kernel-based learning." Statistics Surveys 4 (2010): 148–83. http://dx.doi.org/10.1214/09-ss052.

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37

Wu, Xiaohong, and Jianjiang Zhou. "Fuzzy principal component analysis and its Kernel-based model." Journal of Electronics (China) 24, no. 6 (2007): 772–75. http://dx.doi.org/10.1007/s11767-006-0039-z.

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38

Yang, Hong, Lipeng Gao, and Guohui Li. "Underwater Acoustic Signal Prediction Based on MVMD and Optimized Kernel Extreme Learning Machine." Complexity 2020 (April 24, 2020): 1–17. http://dx.doi.org/10.1155/2020/6947059.

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Aiming at the chaotic characteristics of underwater acoustic signal, a prediction model of grey wolf-optimized kernel extreme learning machine (OKELM) based on MVMD is proposed in this paper for short-term prediction of underwater acoustic signals. To solve the problem of K value selection in variational mode decomposition, a new K value selection method MVMD is proposed from the perspective of mutual information, which avoids the blindness of variational mode decomposition (VMD) in the preset modal number. Based on the prediction model of kernel extreme learning machine (KELM), this paper use

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Lamboni, Matieyendou. "Kernel-based sensitivity indices for any model behavior and screening." Socio-Environmental Systems Modelling 5 (December 12, 2023): 18566. http://dx.doi.org/10.18174/sesmo.18566.

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Complex models are often used to understand interactions and drivers of human-induced and/or natural phenomena. It is worth identifying the input variables that drive the model output(s) in a given domain and/or govern specific model behaviors such as contextual indicators based on socioenvironmental models. Using the theory of multivariate weighted distributions to characterize specific model behaviors, we propose new measures of association between inputs and such behaviors. Our measures rely on sensitivity functionals (SFs) and kernel methods, including variance-based sensitivity analysis.

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CHEN, BADONG, JOSE C. PRINCIPE, JINCHUN HU, and YU ZHU. "STOCHASTIC INFORMATION GRADIENT ALGORITHM WITH GENERALIZED GAUSSIAN DISTRIBUTION MODEL." Journal of Circuits, Systems and Computers 21, no. 01 (2012): 1250006. http://dx.doi.org/10.1142/s0218126612500065.

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This paper presents a parameterized version of the stochastic information gradient (SIG) algorithm, in which the error distribution is modeled by generalized Gaussian density (GGD), with location, shape, and dispersion parameters. Compared with the kernel-based SIG (SIG-Kernel) algorithm, the GGD-based SIG (SIG-GGD) algorithm does not involve kernel width selection. If the error is zero-mean, the SIG-GGD algorithm will become the least mean p-power (LMP) algorithm with adaptive order and variable step-size. Due to its well matched density estimation and automatic switching capability, the prop

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Soliman, Faten Mohamed Ali, Amany Moussa Mohamed, and Mohamed R. Abonazel. "New Robust Estimators for the Nonparametric Regression Model: Application and Simulation Study." International Journal of Analysis and Applications 23 (July 16, 2025): 163. https://doi.org/10.28924/2291-8639-23-2025-163.

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This paper introduces new two robust kernel-based estimators (S Kernel and MM Kernel) for the nonparametric regression mode in the presence of outliers. Through comprehensive simulations, we evaluate their performance using Mean Squared Error (MSE), Mean Absolute Error (MAE), and Relative Efficiency (RE) under varying sample sizes and outlier contamination levels. Results demonstrate that robust estimators consistently outperform traditional kernel estimator, delivering the lowest estimation errors and highest efficiency, particularly in high-contamination scenarios. In contrast, the tradition

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Nie, Junlan, Ruibo Gao, and Ye Kang. "Urban Noise Inference Model Based on Multiple Views and Kernel Tensor Decomposition." Fluctuation and Noise Letters 20, no. 03 (2021): 2150027. http://dx.doi.org/10.1142/s0219477521500279.

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Prediction of urban noise is becoming more significant for tackling noise pollution and protecting human mental health. However, the existing noise prediction algorithms neglected not only the correlation between noise regions, but also the nonlinearity and sparsity of the data, which resulted in low accuracy of filling in the missing entries of data. In this paper, we propose a model based on multiple views and kernel-matrix tensor decomposition to predict the noise situation at different times of day in each region. We first construct a kernel tensor decomposition model by using kernel mappi

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Guo, Changying, Biqin Song, Yingjie Wang, Hong Chen, and Huijuan Xiong. "Robust Variable Selection and Estimation Based on Kernel Modal Regression." Entropy 21, no. 4 (2019): 403. http://dx.doi.org/10.3390/e21040403.

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Model-free variable selection has attracted increasing interest recently due to its flexibility in algorithmic design and outstanding performance in real-world applications. However, most of the existing statistical methods are formulated under the mean square error (MSE) criterion, and susceptible to non-Gaussian noise and outliers. As the MSE criterion requires the data to satisfy Gaussian noise condition, it potentially hampers the effectiveness of model-free methods in complex circumstances. To circumvent this issue, we present a new model-free variable selection algorithm by integrating k

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44

Wang, Ping An, Xu Sheng Gan, and Deng Kai Yao. "Anomaly Intrusion Detection Based on Support Vector Machine with Mexico Hat Wavelet Kernel Function." Applied Mechanics and Materials 687-691 (November 2014): 3897–900. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.3897.

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The selection of kernel function in Support Vector Machine (SVM) has a great influence on the model performance. In the paper, Mexico hat wavelet kernel is introduced to employ the kernel function of SVM, and theoretically it has be prove that, Mexico hat wavelet kernel satisfies the Merce condition, that is the necessary condition as the kernel function of SVM. Simulation on the anomaly detection shows that the capability of SVM based on Mexico hat wavelet kernel is better than that of SVM based on RBF kernel with a satisfactory result for anomaly intrusion detection.

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Tang, Yidong, Shucai Huang, and Aijun Xue. "Sparse Representation Based Binary Hypothesis Model for Hyperspectral Image Classification." Mathematical Problems in Engineering 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/3460281.

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The sparse representation based classifier (SRC) and its kernel version (KSRC) have been employed for hyperspectral image (HSI) classification. However, the state-of-the-art SRC often aims at extended surface objects with linear mixture in smooth scene and assumes that the number of classes is given. Considering the small target with complex background, a sparse representation based binary hypothesis (SRBBH) model is established in this paper. In this model, a query pixel is represented in two ways, which are, respectively, by background dictionary and by union dictionary. The background dicti

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46

Tian, Jinkai, Peifeng Yan, and Da Huang. "Kernel Analysis Based on Dirichlet Processes Mixture Models." Entropy 21, no. 9 (2019): 857. http://dx.doi.org/10.3390/e21090857.

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Kernels play a crucial role in Gaussian process regression. Analyzing kernels from their spectral domain has attracted extensive attention in recent years. Gaussian mixture models (GMM) are used to model the spectrum of kernels. However, the number of components in a GMM is fixed. Thus, this model suffers from overfitting or underfitting. In this paper, we try to combine the spectral domain of kernels with nonparametric Bayesian models. Dirichlet processes mixture models are used to resolve this problem by changing the number of components according to the data size. Multiple experiments have

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Christmann, Andreas, and Ding-Xuan Zhou. "Learning rates for the risk of kernel-based quantile regression estimators in additive models." Analysis and Applications 14, no. 03 (2016): 449–77. http://dx.doi.org/10.1142/s0219530515500050.

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Additive models play an important role in semiparametric statistics. This paper gives learning rates for regularized kernel-based methods for additive models. These learning rates compare favorably in particular in high dimensions to recent results on optimal learning rates for purely nonparametric regularized kernel-based quantile regression using the Gaussian radial basis function kernel, provided the assumption of an additive model is valid. Additionally, a concrete example is presented to show that a Gaussian function depending only on one variable lies in a reproducing kernel Hilbert spac

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Marjani, M., and M. S. Mesgari. "THE LARGE-SCALE WILDFIRE SPREAD PREDICTION USING A MULTI-KERNEL CONVOLUTIONAL NEURAL NETWORK." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W1-2022 (January 14, 2023): 483–88. http://dx.doi.org/10.5194/isprs-annals-x-4-w1-2022-483-2023.

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Abstract. In the last twenty years, destructive wildfires have affected the environment to the tune of billions of dollars. An accurate model is crucial for predicting the spreading of wildfires in a variety of conditions. In this study, a multi-kernel convolution neural network (CNN) deep learning model was proposed based on elevation, wind direction, and speed, minimum and maximum temperatures, humidity, precipitation, drought index, normalized difference vegetation index (NDVI), and energy release component to predict wildfire spread across the United States. Using multi-kernel CNN, it is p

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Jue, Wang. "Prediction model of pulmonary tuberculosis based on gray kernel AR-SVM model." Cluster Computing 22, S2 (2018): 4383–87. http://dx.doi.org/10.1007/s10586-018-1906-8.

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Gao, Xiangbing, Bo Jia, Gen Li, and Xiaojing Ma. "Calorific Value Forecasting of Coal Gangue with Hybrid Kernel Function–Support Vector Regression and Genetic Algorithm." Energies 15, no. 18 (2022): 6718. http://dx.doi.org/10.3390/en15186718.

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The calorific value of coal gangue is a critical index for coal waste recycling and the energy industry. To establish an accurate and efficient calorific value forecasting model, a method based on hybrid kernel function–support vector regression and genetic algorithms is presented in this paper. Firstly, key features of coal gangue gathered from major coal mines are measured and used to build a sample set. Then, the forecasting performance of single kernel function-based models is established, and linear kernel and Gaussian kernel functions are chosen according to forecasting results. Next, a

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