Wenfeng Jing

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The backpropogation (BP) neural networks have been widely applied in scientific research and engineering. The success of the application, however, relies upon the convergence of the training procedure involved in the neural network learning. We settle down the convergence analysis issue through proving two fundamental theorems on the convergence of the(More)
The sparsity driven classification technologies have attracted much attention in recent years, due to their capability of providing more compressive representations and clear interpretation. Two most popular classification approaches are support vector machines (SVMs) and kernel logistic regression (KLR), each having its own advantages. The sparsification(More)
Support vector classification (SVC) is an efficient tool to solve classification which is the fundamental problem in data mining, but its efficiency is limited on the small or middle sized data sets. In this paper a new preprocessing algorithm - clarifier method (CM) is proposed to accelerate training of SVC especially when training data are of large mode.(More)
The uniformly pseudo-projection-anti-monotone (UPPAM) neural network model, which can be considered as the unified continuous-time neural networks (CNNs), includes almost all of the known CNNs individuals. Recently, studies on the critical dynamics behaviors of CNNs have drawn special attentions due to its importance in both theory and applications. In this(More)
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