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Support vector machine (SVM) is a machine learning method developed in the mid-1990s based on statistical learning theory. SVM classifier is currently more popular classifier. This paper presents a boundary detection technique for retaining the potential support vector. Through seeking to structural risk minimization of the SVM, it improves the learning generalization ability and achieves the minimization...
Credit risk assessment is a basic and critical factor in credit risk management. In addition to conventional statistical method, neural network, decision tree and Support Vector Machine are the popular methods in this field in recent years. However, they all have weakness in two aspects: poor classification accuracy for unbalanced data and poor interpretability in real applications. A novel method,...
In many cases, data are represented as high dimensional feature vectors. It makes the feature selection necessary to reduce the computational burden, improve the generalization ability and the interpretability. In this paper, we present a novel feature selection method which is named least squares support feature machine (LS-SFM). In comparison with SVM and LS-SVM, this method has two outstanding...
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