Support vector regression model of a wide range microwave transistor

Applications in learning machine is a searching subject which has been widely worked on. The studies have enabled improving the powerful classification algorithms such as Support Vector Machines (SVM) which are based on statistical learning theory, and the succesful usage of SVM in really different areas. It is has been showed that Support Vector Machines are more robust algorithm than the classic classification algorithm in many applicaitons. The aim of this thesis study is to apply the SVM into the microwave theory and technique. Therefore in this thesis, a wide range microwave transistor is analysed with SVRM. In the analysis of a microwave transistor is utilized the capability of SVM's making regression and sparseness feature. For the model's regression, our machine has been trained and tested to achieve high accuracy.