The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Individual credit risk evaluation is an important and challenging data mining problem in financial analysis domain. This paper compares the effectiveness of four data mining algorithms - logistic regression (LR), decision tree (C4.5), support vector machine (SVM) and neural networks (NN) by applying them to two credit data sets. Experiment results show that the LR and SVM algorithms produced the best...
Generalization performance of support vector machines (SVM) is affected by parameter selection. How to select optimal parameters to achieve the best training model has been a hot research spot. In order to improve generalization performance of SVM, K-fold cross validation is used to select parameters for training. However, K-fold cross validation is time-consuming, especially for large number of samples,...
The location of logistic center directly influences the operational effect of the enterprise. Support vector machine (SVM) has been applied to regression widely. However, if the index of the training data has much noise and redundancy, the generalized performance of SVM will be weakened, so this can cause some disadvantages of slow convergence speed and low regression accuracy. A SVM regression model...
Based on the statistical learning theory support vector machine focuses on the machine learning strategies under small samples and gets better generalization ability than those tools based on the experience risk minimization principle. Its classing or regression performance will be affected by relative super-parameters. An improved multi object optimization algorithm based on simulated annealing is...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.