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Least squares support vector machines (LSSVM) has a good performance in small data samples, but can't solve the large-scale sample problems. In this paper, large data set sparse least squares support vector machines model based on stochastic entropy is proposed, and it can be applied to large-scale data samples. Firstly, the large-scale data set is divided into several subsets. Then the entropy method...
Multi-task clustering and multi-view clustering have severally found wide applications and received much attention in recent years. Nevertheless, there are many clustering problems that involve both multi-task clustering and multi-view clustering, i.e., the tasks are closely related and each task can be analyzed from multiple views. In this paper, we introduce a multi-task multi-view clustering framework...
A smart home control system could link independent electric appliances together 'intelligently' and provide comprehensive, fast and smooth exchange of information. This system gives people real-time information control, which improves their life quality. In this study, an embedded smart home system based on socket network connections was designed using ARM9 development board and embedded Linux operating...
Tax assessment has always been regarded as a tool to check whether taxpayers submit the right amount of money. This paper propose a new tax assessment model mainly based on an attribute reduction SVM in the light of its good performance in classifying high-dimensional nonlinear data. In the paper, we apply our model to a real-world example to see its practical performance. The results show that our...
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