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The least squares support vector machine (LS-SVM) has emerged as a popular data-driven modeling method and been extensively studied in the machine learning community. However, the LS-SVM is sensitive to noisy data and may not be effective when the level of noise is high. In this paper, a probabilistic LS-SVM is proposed to have a more reliable performance. First, a distributed LS-SVM is constructed...
The support vector machine (SVM) has a good generalization performance, but the classification result of the SVM in some real problems is often unsatisfied. Because SVM is sensitive to the noisy data and it may not be effective under the high level of noise. To improve the performance of SVM in the noisy environment, we propose an ensemble learning model to address the noise problem in this work....
In the real world, uncertainty in the data is a frequently confronted difficulty problem for learning system. The performance of the learning method can be deteriorated by the uncertainty. To properly represent and handle the uncertainty problem becomes one of the key issues in the decision learning field. An intelligent learning model is presented in this paper to address the uncertainty problem...
A probabilistic support vector machine (PSVM) is proposed for classification of data with uncertainties. Performance of the traditional SVM algorithm is very sensitive to uncertainties. The noises in input space will cause uncertainties of the mapping in feature space. The traditional SVM algorithm may not be effective when uncertainty is large. A new probabilistic optimization is proposed to determine...
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