Preemption of a CUDA Kernel Function

2012 ◽  
Author(s):  
Jon Calhoun ◽  
Hai Jiang
Keyword(s):  
Kernel Function ◽  
Cuda Kernel

Application of various kernel function based SVM in spam filtering

2008 ◽  
Vol 28 (2) ◽  
pp. 424-427
Author(s):  
Jian-she DONG
Keyword(s):  
Kernel Function ◽  
Spam Filtering

Algorithm of target tracking based on Mean-Shift with adaptive bandwidth of kernel function

2009 ◽  
Vol 29 (6) ◽  
pp. 1680-1682
Author(s):  
Chang-tao CHEN ◽  
Qin ZHU ◽  
Sheng-yi ZHOU ◽  
Jia-ming ZHANG
Keyword(s):  
Target Tracking ◽  
Kernel Function ◽  
Mean Shift ◽  
Adaptive Bandwidth

Parameters optimization of combined kernel function for support vector machine

2013 ◽  
Vol 33 (5) ◽  
pp. 1321-1323 ◽  
Author(s):  
Junbao GENG ◽  
Linkai SUN ◽  
Shixue CHEN
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Parameters Optimization ◽  
Support Vector

Remaining Useful Life Prediction of Lithium-ion Batteries Using Multiple Kernel Extreme Learning Machine

2020 ◽  
Vol 13 ◽  
Author(s):  
Renxiong Liu
Keyword(s):  
Lithium Ion Batteries ◽  
Extreme Learning Machine ◽  
Kernel Function ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Prediction Methods ◽  
Multiple Kernel ◽  
Kernel Extreme Learning Machine ◽  
Useful Life ◽  
Learning Machine

Objective: Lithium-ion batteries are important components used in electric automobiles (EVs), fuel cell EVs and other hybrid EVs. Therefore, it is greatly important to discover its remaining useful life (RUL). Methods: In this paper, a battery RUL prediction approach using multiple kernel extreme learning machine (MKELM) is presented. The MKELM’s kernel keeps diversified by consisting multiple kernel functions including Gaussian kernel function, Polynomial kernel function and Sigmoid kernel function, and every kernel function’s weight and parameter are optimized through differential evolution (DE) algorithm. Results : Battery capacity data measured from NASA Ames Prognostics Center are used to demonstrate the prediction procedure of the proposed approach, and the MKELM is compared with other commonly used prediction methods in terms of absolute error, relative accuracy and mean square error. Conclusion: The prediction results prove that the MKELM approach can accurately predict the battery RUL. Furthermore, a compare experiment is executed to validate that the MKELM method is better than other prediction methods in terms of prediction accuracy.

Optimization of combined kernel function for SVM based on large margin learning theory

2008 ◽  
Author(s):  
Mingzhu Lu ◽  
Jianbing Huo ◽  
C. L. Philip Chen ◽  
Xizhao Wang
Keyword(s):  
Kernel Function ◽  
Learning Theory ◽  
Large Margin

scholarly journals Improved Predictive Ability of KPLS Regression with Memetic Algorithms

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 506
Author(s):  
Jorge Daniel Mello-Román ◽  
Adolfo Hernández ◽  
Julio César Mello-Román
Keyword(s):  
Kernel Function ◽  
Linear Method ◽  
Predictive Ability ◽  
Feature Space ◽  
Memetic Algorithms ◽  
Least Squares Regression ◽  
Optimal Parameters ◽  
Number Of Components ◽  
Kernel Partial Least Squares ◽  
Dependent Variables

Kernel partial least squares regression (KPLS) is a non-linear method for predicting one or more dependent variables from a set of predictors, which transforms the original datasets into a feature space where it is possible to generate a linear model and extract orthogonal factors also called components. A difficulty in implementing KPLS regression is determining the number of components and the kernel function parameters that maximize its performance. In this work, a method is proposed to improve the predictive ability of the KPLS regression by means of memetic algorithms. A metaheuristic tuning procedure is carried out to select the number of components and the kernel function parameters that maximize the cumulative predictive squared correlation coefficient, an overall indicator of the predictive ability of KPLS. The proposed methodology led to estimate optimal parameters of the KPLS regression for the improvement of its predictive ability.

scholarly journals Application of Projection Pursuit Analysis Method Based on Kernel Function in Fault Diagnosis for Rolling Bearing

2021 ◽  
Vol 770 (1) ◽  
pp. 012001
Author(s):  
Cheng Jing ◽  
Su Le ◽  
Wang Weiqing ◽  
He Shan
Keyword(s):  
Fault Diagnosis ◽  
Kernel Function ◽  
Projection Pursuit ◽  
Rolling Bearing ◽  
Analysis Method
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