New Mixed Kernel Functions of SVM Used in Pattern Recognition

Hao, Huanrui

doi:10.1515/cait-2016-0047

Cited by 8 publications

(7 citation statements)

References 10 publications

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“…Poly kernel is a global kernel function that has a significant influence on dots that are far apart; it has extremely strong generalizability but weak learning capacity. By contrast, Gaussian RBF is a local kernel function that only influences dots that are relatively close; it has strong learning capacity but weak generalizability (Huanrui, 2016;Liu et al, 2016).…”

Section: Pso Hybrid Kernel Svr (Pso-hk-svr)mentioning

confidence: 99%

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PM2.5 Concentration Prediction Based on Markov Blanke Feature Selection and Hybrid Kernel Support Vector Regression Optimized by Particle Swarm Optimization

Zhang

Deng

2021

Aerosol Air Qual. Res.

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This study employed air quality and meteorological data as research materials and extracted the optimal feature subset by using the approximate Markov blanket-based normal maximum relevance minimum redundancy (nMRMR) algorithm to serve as the input data of the prediction model. In addition, a hybrid kernel (HK) was created to improve upon the traditional support vector regression (SVR) model. Particle swarm optimization (PSO) was used to calculate the

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Section: Pso Hybrid Kernel Svr (Pso-hk-svr)mentioning

confidence: 99%

“…To create a kernel that influences close-together dots as well as far-apart dots in fuzzy prediction, Poly and the Gaussian RBF kernel can be weighted and integrated to formulate a new linear HK (Huanrui, 2016):…”

Section: Pso Hybrid Kernel Svr (Pso-hk-svr)mentioning

confidence: 99%

PM2.5 Concentration Prediction Based on Markov Blanke Feature Selection and Hybrid Kernel Support Vector Regression Optimized by Particle Swarm Optimization

Zhang

Deng

2021

Aerosol Air Qual. Res.

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“…SVM is used as a classifier by [25] for gender classification using oriented basic image features where SVM is used with kernel parameter [50] that is selected in the range[0, 100] while the soft margin parameter C that is selected as 10.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

A Study on Various Techniques Involved in Gender Prediction System: A Comprehensive Review

Maken

Gupta

2019

Cybernetics and Information Technologies

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Predicting gender on the foundation of handwriting investigation is a very invoking research area. Handwriting analysis also has numerous applications. It is useful for forensic experts to investigate classes of writers. This prediction is executed fundamentally by two steps: feature extraction and classification. As a whole, prediction solely depends upon the feature extraction. Distinct and most varied features make the classification accurate. This paper describes the problem comprehensively along with its foundation. It presents a survey of the available methods to solve the discussed problem. A comparative analysis of the discussed methods of feature extraction and classification along with the available databases is also presented in this paper.

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“…In the formula (3), k(m x i , m x j ) is the kernel function, the Gaussian radial basis kernel function k RBF (m x i , m x j ) and the polynomial kernel function k poly (m x i , m x j ) are combined to construct the mixed kernel function as follows [14]:…”

Section: Mathematical Model Of Fv-svrmentioning

confidence: 99%

Reliability analysis of blade fatigue life based on fuzzy intelligent multiple extremum response surface method

Zhang¹,

Sun²,

Wang³

et al. 2018

Filomat

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In order to more reasonable analyze the dynamic reliability of aero-engine blade with coupling failure mode. A fuzzy intelligent multiple extremum response surface method (FIMERSM) was proposed. Considering the coupling effect of temperature load and centrifugal load, the maximum stress point, the maximum strain point and the minimum life point on blade were found by deterministic analysis. Then, the density of blade, rotor speed, elastic modulus, blade-tip temperature, blade-root temperature, fatigue strength coefficient, fatigue strength exponent, fatigue ductility coefficient, fatigue ductility exponent, blade width, blade thickness, blade torsion angle, and blade height as input random variables. By using Latin hypercube sampling technique, the sample values of the input random variables were acquired and finite element basic equation was calculated for each samples which obtained the corresponding dynamic output response of their stress, strain, and low cycle fatigue life within the analysis time domain. By taking the entire maximum values of the dynamic output response in the analysis time domain as new output response, the fuzzy intelligent multiple extremum response surface function (FIMERSF) was established. Finally, the dynamic reliability of the blade structure were obtained by using the Monte Carlo method (MCM) large amount linkage sampling of the input random variables and take it into the FIMERSF to calculate the output response. The results imply that the comprehensive reliability of blade is 99.46%. Through the comparison of MCM, Multiple extremum response surface method (MERSM) and FIMERSM, the computational results show that the FIMERSM has high computational precision and computational efficiency.

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New Mixed Kernel Functions of SVM Used in Pattern Recognition

Cited by 8 publications

References 10 publications

PM2.5 Concentration Prediction Based on Markov Blanke Feature Selection and Hybrid Kernel Support Vector Regression Optimized by Particle Swarm Optimization

PM2.5 Concentration Prediction Based on Markov Blanke Feature Selection and Hybrid Kernel Support Vector Regression Optimized by Particle Swarm Optimization

A Study on Various Techniques Involved in Gender Prediction System: A Comprehensive Review

Reliability analysis of blade fatigue life based on fuzzy intelligent multiple extremum response surface method

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