scholarly journals Optimization of Support Vector Regression Parameters by Flower Pollination Algorithm

2017 ◽  
Author(s):  
Yuan Yang ◽  
Zhongqi Wang ◽  
Bo Yang ◽  
Xudong Liu
Keyword(s):  
Support Vector Regression ◽  
Flower Pollination Algorithm ◽  
Support Vector ◽  
Flower Pollination ◽  
Regression Parameters

Optimisation of Support Vector Regression Parameters

NIR news ◽  
2006 ◽  
Vol 17 (4) ◽  
pp. 12-13
Author(s):  
Bülent Üstün ◽  
Willem Melssen ◽  
Lutgarde Buydens
Keyword(s):  
Support Vector Regression ◽  
Support Vector ◽  
Regression Parameters

scholarly journals Parameter Selection Method for Support Vector Regression Based on Adaptive Fusion of the Mixed Kernel Function

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Hailun Wang ◽  
Daxing Xu
Keyword(s):  
Kalman Filter ◽  
Support Vector Regression ◽  
Kernel Function ◽  
Unscented Kalman Filter ◽  
Selection Method ◽  
Parameter Selection ◽  
Support Vector ◽  
Regression Parameters ◽  
Cubature Kalman Filter ◽  
Adaptive Fusion

Support vector regression algorithm is widely used in fault diagnosis of rolling bearing. A new model parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function is proposed in this paper. We choose the mixed kernel function as the kernel function of support vector regression. The mixed kernel function of the fusion coefficients, kernel function parameters, and regression parameters are combined together as the parameters of the state vector. Thus, the model selection problem is transformed into a nonlinear system state estimation problem. We use a 5th-degree cubature Kalman filter to estimate the parameters. In this way, we realize the adaptive selection of mixed kernel function weighted coefficients and the kernel parameters, the regression parameters. Compared with a single kernel function, unscented Kalman filter (UKF) support vector regression algorithms, and genetic algorithms, the decision regression function obtained by the proposed method has better generalization ability and higher prediction accuracy.

Using Multi-objective Algorithms for Optimizing Support Vector Regression Parameters

2018 ◽  
Author(s):  
Manoel Alves de Almeida Neto ◽  
Roberta Andrade A. de de Fagundes ◽  
Carmelo J. A. Bastos-Filho
Keyword(s):  
Support Vector Regression ◽  
Support Vector ◽  
Multi Objective ◽  
Regression Parameters

Determination of optimal support vector regression parameters by genetic algorithms and simplex optimization

2005 ◽  
Vol 544 (1-2) ◽  
pp. 292-305 ◽  
Author(s):  
B. Üstün ◽  
W.J. Melssen ◽  
M. Oudenhuijzen ◽  
L.M.C. Buydens
Keyword(s):  
Genetic Algorithms ◽  
Support Vector Regression ◽  
Support Vector ◽  
Simplex Optimization ◽  
Regression Parameters

scholarly journals Hybrid Flower Pollination Algorithm and Support Vector Machine for Breast Cancer Classification

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Muhammad Nasiru Dankolo ◽  
◽  
Nor Haizan Mohamed Radzi ◽  
Roselina Salehuddin ◽  
Noorfa Haszlinna Mustaffa
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Cancer Classification ◽  
Flower Pollination Algorithm ◽  
Support Vector ◽  
Breast Cancer Classification ◽  
Flower Pollination
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