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The selection for hyper-parameters is difficult and important to the performance of least squares support vector machines (LS-SVM). The existed parameters selection algorithms, such as the analytical, algebraic techniques and particle swarm optimization (PSO) algorithm, have their own shortcomings. In this paper, the problem of model selection for LS-SVM is discussed and a new method selecting the...
In order to resolve decision classification problem in multiple agents system, this paper first introduces the architecture of multiple agents system. It then proposes a support vector machines based assessment approach, which has the ability to learn the rules form previous assessment results from domain experts. Finally, the experiment are conducted on the artificially dataset to illustrate how...
In SVMs-based multiple classification, it is not always possible to find an appropriate kernel function to map all the classes from different distribution functions into a feature space where they are linearly separable from each other. This is even worse if the number of classes is very large. As a result, the classification accuracy is not as good as expected. In order to improve the performance...
Data mining in the CRM aiming at learning available knowledge from the customer relationship by machine learning or statistical method to instruct the strategic behavior so that obtain the most profit. In recent years, Support vector machine (SVMs) has been proposed as a power tool in machine leaning and data mining. This paper applies the SVMs to resolve the practical CRM problem in a company. The...
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