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Support vector machine (SVM) is a promising method of machine learning based on the structural risk minimization principle, which is characteristic of good generalization performance; Rough set (RS) is an effective tool to decrease data dimension in dealing with vagueness and uncertainty information. A SVM classifier based on RS reducts is researched in order to enhance the predicting performance...
In this paper, we use particle swarm optimization with support vector machine optimized to evaluate the investment risk of electrical project. A hybrid intelligent system is applied to evaluation of electrical equipment, combining particle swarm optimize algorithm (PSO) and support vector machines (SVM). At first, we can make use of PSO obtaining appropriate parameters in order to improve the general...
A new prediction model that combining the merits of support vector machine (SVM) and gray RBF neutral network is proposed in this paper. First apply structural risk minimization principle to optimize the modeling method of RBF neutral network, so that the radial basis centers and network weights could be acquired directly. Then use error compensator of RBF neutral network based on structural risk...
Based on the comprehensive analysis of the existing risk early warning methods in real estate, a new risk forecast method based on support vector machine is put forward. And a risk early warning model in real estate market based on support vector machine is established. The realization process of the risk early warning method is discussed. Taking the practical data in real estate exploitation as the...
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