Parameter Selection for Sub-hyper-sphere Support Vector Machine

2007 ◽  
Author(s):  
Peng Chen ◽  
Tao Wen
Keyword(s):  
Support Vector Machine ◽  
Parameter Selection ◽  
Support Vector ◽  
Selection For

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.

Novel linear search for support vector machine parameter selection

2011 ◽  
Vol 12 (11) ◽  
pp. 885-896 ◽  
Author(s):  
Hong-xia Pang ◽  
Wen-de Dong ◽  
Zhi-hai Xu ◽  
Hua-jun Feng ◽  
Qi Li ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Parameter Selection ◽  
Support Vector ◽  
Machine Parameter ◽  
Linear Search ◽  
Support Vector Machine Parameter
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