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According to the complexity of financial system, the model of discrimination for non-performing loans recovery based on support vector machines(SVM) and wavelet transform integrated is proposed, which in order to improve the accuracy and reliability of risk assessment. First we performed model input optimization using the wavelet transform, and then selected the radial basis function(RBF) as the kernel...
These instructions A new intelligent fault diagnosis (IFD) method based on evolutionary algorithm and support vector machines (SVM) for multivariate process monitoring was proposed. A hybrid method combining feature selection and generation in a wrapper based approach via evolutionary algorithm was proposed to automatically generate feature set, and SVM was proposed to serve as an inductive learner...
It is well recognized that support vector machines (SVM) would produce better classification performance in terms of generalization power. Based on the statistical learning theory (SLT), the margin scale reflects the generalization capability to a great extent. The bigger the margin scale takes, the better the generalization capability of SVM will have. This paper makes an attempt to investigate the...
As one kind of one-class classifier, support vector domain classifier (SVDC) has worked well for the batch model learning problems. But with real-world database increase in size, there is a need to scale up inductive learning algorithm to handle more training data. On-line learning technique is one possible solution to the scalability problem, where data is processed in parts, and the result combined...
Learning is obtaining an underlying rule by using training data sampled from the environment. In many practical situations in inductive learning algorithms, it is often expected to further improve the generalization capability after the learning process has been completed if new data are available. One of the common approaches is to add training data to the learning algorithm and retrain it, but retraining...
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