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Long-term time series prediction is to predict the future values multi-step ahead. It has received more and more attention due to its applications in predicting stock prices, traffic status, power consumption, etc. In this paper, a k-nearest neighbors (k-NN) based least squares support vector machine (LS-SVM) framework is proposed to perform long-term time series prediction. A new distance function,...
In case-based reasoning (CBR), cases are generally represented by features. Different features have different importance, which are often described by weights. So how to adaptively learning weights of different features is a very key issue in CBR, which impact directly the quality and performance of case extraction. Currently, in most practical CBR systems, the feature weights are given by domain...
An adaptive and iterative LSSVR algorithm based on quadratic Renyi entropy is presented in this paper. LS-SVM loses the sparseness of support vector which is one of the important advantages of conventional SVM. The proposed algorithm overcomes this drawback. The quadratic Renyi entropy is the evaluating criterion for working set selection, and the size of working set is determined at the process of...
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