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Deep LS-SVM for regression
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 Title & Authors
Deep LS-SVM for regression
Hwang, Changha; Shim, Jooyong;
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In this paper, we propose a deep least squares support vector machine (LS-SVM) for regression problems, which consists of the input layer and the hidden layer. In the hidden layer, LS-SVMs are trained with the original input variables and the perturbed responses. For the final output, the main LS-SVM is trained with the outputs from LS-SVMs of the hidden layer as input variables and the original responses. In contrast to the multilayer neural network (MNN), LS-SVMs in the deep LS-SVM are trained to minimize the penalized objective function. Thus, the learning dynamics of the deep LS-SVM are entirely different from MNN in which all weights and biases are trained to minimize one final error function. When compared to MNN approaches, the deep LS-SVM does not make use of any combination weights, but trains all LS-SVMs in the architecture. Experimental results from real datasets illustrate that the deep LS-SVM significantly outperforms state of the art machine learning methods on regression problems.
Deep learning;hidden layer;least squares support vector machine;multilayer neural network;penalized objective function;
 Cited by
두 이종 혼합 모형에서의 수정된 경사 하강법,문상준;전종준;

Journal of the Korean Data and Information Science Society, 2017. vol.28. 6, pp.1245-1255 crossref(new window)
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