Abstract
Support vector machines (SVMs) are a promising type of learning machine based on structural risk minimization and statistical learning theory, which can be divided into two categories: support vector classification (SVC) machines and support vector regression machines (SVR). The basic elements and algorithms of SVC machines are discussed. As modeling and prediction methods are introduced into the experiment of microwave calcining AUC, the better prediction accuracy and the better fitting results are compare with back propagation (BP) neural network method. This is conducted to elucidate the good generalization performance of SVMs, especially good for dealing with the data of some nonlinearity.
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