Selecting Hyperparameters of Nonlinear Support Vector Machine Using Bayesian Inference

Author:

Jang Jonghyok1ORCID,Jang Uijong1ORCID,Jong Myongguk1ORCID

Affiliation:

1. Kim Chaek University of Technology, Democratic People??s Republic of Korea

Publisher

ACM

Reference24 articles.

1. The Nature of Statistical Learning Theory

2. Jianfeng Wang,Lei Zhang, “A parameter optimization method for an SVM based on improved grid search algorithm,” Applied Science and Technology, Vol.39,No.3 ,2012.

3. Chapelle, An efficient method for gradient-based adaptation of hyperparameters in SVM models;Keerthi S.;Adv. Neural Inf. Process. Syst,2007

4. Tuning SVM parameters by using a hybrid CLPSO–BFGS algorithm

5. ACO-based hybrid classification system with feature subset selection and model parameters optimization

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