A parameter selection of support vector machine with genetic algorithm for citrus quality classification

2011 ◽  
Author(s):  
Tang Guoxiang ◽  
Qu Ming ◽  
Wang Xuan ◽  
Lv Jiake
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Parameter Selection ◽  
Support Vector ◽  
Quality Classification ◽  
Selection Of

Performance Prediction by an SVM with a Firefly Optimization Method

2020 ◽  
pp. 2150017
Author(s):  
Yongquan Yan
Keyword(s):  
Support Vector Machine ◽  
Economic Loss ◽  
Optimization Method ◽  
Parameter Selection ◽  
Support Vector ◽  
Software Aging ◽  
Memory Prediction ◽  
Firefly Optimization ◽  
Aging Phenomena ◽  
Selection Of

Since software system is becoming more and more complex than before, performance degradation and even abrupt download, which are called software aging phenomena, bring about a great deal of economic loss. To counter these problems, some methods are used. Support vector machine is an effective method to tackle software aging problems, but its performance is influenced by the selection of hyper-parameters. A method is proposed to optimize the hyper-parameter selection of support vector machine in this work. The proposed method which is used as a training algorithm to optimize the parameter selection of support vector machine, utilizes the global exploration power of firefly method to achieve faster convergence and also a better accuracy. In the experiment, we use two metrics to test the effect of the proposed method. The results indicate that the presented method owns the highest accuracy in both the available memory prediction and heap memory prediction of Web server for software aging predictions.

scholarly journals Parameter Selection of a Support Vector Machine, Based on a Chaotic Particle Swarm Optimization Algorithm

2015 ◽  
Vol 15 (3) ◽  
pp. 140-149 ◽  
Author(s):  
Huang Dong ◽  
Gao Jian
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Parameter Selection ◽  
Support Vector ◽  
Optimal Parameters ◽  
Swarm Optimization ◽  
Chaotic Particle Swarm Optimization ◽  
Selection Of

Abstract This paper proposes a SVM (Support Vector Machine) parameter selection based on CPSO (Chaotic Particle Swarm Optimization), in order to determine the optimal parameters of the support vector machine quickly and efficiently. SVMs are new methods being developed, based on statistical learning theory. Training a SVM can be formulated as a quadratic programming problem. The parameter selection of SVMs must be done before solving the QP (Quadratic Programming) problem. The PSO (Particle Swarm Optimization) algorithm is applied in the course of SVM parameter selection. Due to the sensitivity and frequency of the initial value of the chaotic motion, the PSO algorithm is also applied to improve the particle swarm optimization, so as to improve the global search ability of the particles. The simulation results show that the improved CPSO can find more easily the global optimum and reduce the number of iterations, which also makes the search for a group of optimal parameters of SVM quicker and more efficient.

Parameter selection of support vector machines and genetic algorithm based on change area search

2011 ◽  
Vol 21 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Mingyuan Zhao ◽  
Jian Ren ◽  
Luping Ji ◽  
Chong Fu ◽  
Jianping Li ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machines ◽  
Parameter Selection ◽  
Support Vector ◽  
Vector Machines ◽  
Selection Of
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