AN EFFICIENT ONLINE LEARNING APPROACH FOR SUPPORT VECTOR REGRESSIONReportar como inadecuado




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1 LGI - Laboratoire Génie Industriel - EA 2606 2 Chaire Sciences des Systèmes et Défis Energétiques EDF-ECP-Supélec LGI - Laboratoire Génie Industriel - EA 2606, SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF-ECP-Supélec 3 Department of Biostatistics Department of Biostatistics, University of Oslo 4 EDF R&D - EDF Division Recherche et Développement Clamart

Abstract : In this paper, an efficient online learning approach is proposed for Support Vector Regression SVR by combining Feature Vector Selection FVS and incremental learning. FVS is used to reduce the size of the training data set and serves as model update criterion. Incremental learning can -adiabatically- add a new Feature Vector FV in the model, while retaining the Kuhn-Tucker conditions. The proposed approach can be applied for both online training & learning and offline training & online learning. The results on a real case study concerning data for anomaly prediction in a component of a power generation system show the satisfactory performance and efficiency of this learning paradigm.





Autor: Jie Liu - Valeria Vitelli - Redouane Seraoui - Enrico Zio -

Fuente: https://hal.archives-ouvertes.fr/



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