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Approximate training of one-class support vector machines using expected margin
Kang, Seokho,Kim, Dongil,Cho, Sungzoon Elsevier 2019 COMPUTERS & INDUSTRIAL ENGINEERING Vol.130 No.-
<P><B>Abstract</B></P> <P>One-class support vector machine (OCSVM) has demonstrated superior performance in one-class classification problems. However, its training is impractical for large-scale datasets owing to high computational complexity with respect to the number of training instances. In this study, we propose an approximate training method based on the concept of expected margin to obtain a model identical to full training with reduced computational burden. The proposed method selects prospective support vectors using multiple OCSVM models trained on small bootstrap samples of an original dataset. The final OCSVM model is trained using only the selected instances. The proposed method is not only simple and straightforward but also considerably effective in improving the training efficiency of OCSVM. Preliminary experiments are conducted on large-scale benchmark datasets to examine the effectiveness of the proposed method in terms of approximation performance and computational efficiency.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Approximate training method for one-class support vector machine is presented. </LI> <LI> It selects instances likely to be support vectors using expected margin. </LI> <LI> Final model is trained only using selected instances. </LI> <LI> Training time is reduced without sacrificing predictive performance. </LI> <LI> Effectiveness is demonstrated on large-scale datasets. </LI> </UL> </P>
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Robust Nonlinear Model Predictive Control via Approximate Value Function
Yu Yang,Jong Min Lee 제어로봇시스템학회 2011 제어로봇시스템학회 국제학술대회 논문집 Vol.2011 No.10
In order to improve the performance of nonlinear model predictive control (NMPC) in the presence of disturbances or model uncertainties, an approximate dynamic programming (ADP) control scheme is proposed. Namely, the Bellman’s optimality principle is employed to determine the input based on the approximate value function constructed from the historical operation data. In addition, the support vector data description is also applied in the state space to determine if the ADP control is suitable for the current state. The proposed control strategy is illustrated on a CSTR example to show its effectiveness.
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