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Relevant Vector Machine (RVM) and Support Vector Machine (SVM) are two relatively new methods that enable us to utilize a few experimental sample points to construct an explicit metamodel. They have been extensively employed in both classification and regression problems. However, their performance in uncertainty analysis is rarely studied. The focus of this paper is to compare the two metamodeling...
In this paper, a novel approach for neural network ensemble called Simple Ensemble of Extreme Learning Machine (SE-ELM) is proposed. It is proved theoretically in this study that the generalization ability of an ensemble is determined by the diversity of its components' output space. Therefore SE-ELM regards the diversity of components' output space as a target during the training process. In the...
There are many labeled data sets which have an unbalanced representation among the classes in them. When the imbalance is large, classification accuracy on the smaller class tends to be lower. In particular, when a class is of great interest but occurs relatively rarely such as cases of fraud, instances of disease, and so on, it is important to accurately identify it. Here we propose a novel algorithm...
With the increase of the training set??s size, the efficiency of support vector machine (SVM) classifier will be confined. To solve such a problem, a novel pre-extracting method for SVM classification is proposed in this paper. In SVM classification, only support vectors (SVs) have significant influence on the optimization result. We adopt a non-parametric k-NN rule called relative neighborhood graph...
Along with the development of the Internet, how to manage and control network information resources effectively has become a hot research. In this paper, we discussed key technologies of network information filtering: feature selection and learning algorithm. Based on feature subset generated by feature selection would affect the filtering accuracy, we proposed an improved feature selection method...
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