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A predictive model of water-quality, which based on wavelet transform and support vector machine, is proposed. This model uses wavelet transform to get water time sequence variations in different scale, and optimizes three parameters of Regression Support Vector Machine with improved Particle Swarm Optimization algorithm, to improve the accuracy of prediction model. This model is used to take one-step...
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,...
SVM which is based on statistical theory has the advantage of no relying on designer's experience of learning and the prior knowledge. So it is widely used in optimization, decision-making, regression estimates, speech recognition, facial image recognition, and so on. Because there are some kinds of wrong and isolated samples in the training samples in the forecasting model, and the learning process...
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