A New Support Vector Machine Optimized by Simulated Annealing for Global Optimization | Zendy

Jiayang Wang | Zendy; Wensheng Wang | Zendy; Shaogui Wu | Zendy

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A New Support Vector Machine Optimized by Simulated Annealing for Global Optimization

Author(s) -

Jiayang Wang,

Wensheng Wang,

Shaogui Wu

Publication year - 2012

Publication title -

international journal of engineering and manufacturing

Language(s) - English

Resource type - Journals

eISSN - 2306-5982

pISSN - 2305-3631

DOI - 10.5815/ijem.2012.01.02

Subject(s) - support vector machine , simulated annealing , blindness , computer science , kernel (algebra) , adaptive simulated annealing , annealing (glass) , key (lock) , algorithm , machine learning , artificial intelligence , data mining , mathematical optimization , mathematics , materials science , medicine , computer security , combinatorics , optometry , composite material

SA-SVM model was proposed in which parameters were optimized by simulated annealing. Parameter (the kernel function) and C (the error discipline) are the key factors to the precision of SVM. Simulated annealing was used to optimize the key parameters of SVM to make enhancement on the forecasting effect of SVM. By applying this proposed model for several function optimizations, results of which demonstrate the improvement of SA-SVM on the high model accuracy in the optimization searching, and it can overcome the blindness of the model parameters.

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