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Prediction Performance of Hybrid Least Square Support Vector Machine with First Principle Knowledge  

김병주 (영산대학교 컴퓨터 정보공학부)
심주용 (대구가톨릭대학교 정보통계학과)
황창하 (대구가톨릭대학교 정보통계학과)
김일곤 (경북대학교 컴퓨터과학과)
Publication Information
Journal of KIISE:Software and Applications / v.30, no.7_8, 2003 , pp. 744-751 More about this Journal
Abstract
A hybrid least square Support Vector Machine combined with First Principle(FP) knowledge is proposed. We compare hybrid least square Support Vector Machine(HLS-SVM) with early proposed models such as Hybrid Neural Network(HNN) and HNN with Extended Kalman Filter(HNN-EKF). In the training and validation stage HLS-SVM shows similar performance with HNN-EKF but better than HNN, whereas, in the testing stage, it shows three times better than HNN-EKF, hundred times better than HNN model.
Keywords
first principle; hybrid least squares support vector machine; neural network; extended Kalman filter;
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