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  • IdHAL : jie-liu

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Article dans une revue8 documents

  • Jie Liu, Enrico Zio. A SVR-based ensemble approach for drifting data streams with recurring patterns. Applied Soft Computing, Elsevier, 2016, <10.1016/j.asoc.2016.06.030>. <hal-01342890>
  • Jie Liu, Enrico Zio. Feature vector regression with efficient hyperparameters tuning and geometric interpretation. Neurocomputing, Elsevier, 2016, 218, pp.411 - 422. <10.1016/j.neucom.2016.08.093>. <hal-01408779>
  • Jie Liu, Enrico Zio. An adaptive online learning approach for Support Vector Regression: Online-SVR-FID. Mechanical Systems and Signal Processing, Elsevier, 2016, 76-77, pp.796 - 809. <10.1016/j.ymssp.2016.02.056>. <hal-01408776>
  • Jie Liu, Enrico Zio. SVM hyperparameters tuning for recursive multi-step-ahead prediction. Neural Computing and Applications, Springer Verlag, 2016. <hal-01342889>
  • Jie Liu, Valeria Vitelli, Enrico Zio, Redouane Seraoui. A Novel Dynamic-Weighted Probabilistic Support Vector Regression-Based Ensemble for Prognostics of Time Series Data. IEEE Transactions on Reliability, Institute of Electrical and Electronics Engineers, 2015, PP (99), pp.11. <10.1109/TR.2015.2427156>. <hal-01176316>
  • Fenglai Liu, Ester Livshits, Rohini C. Lochan, Arne Luenser, Prashant Manohar, et al.. Advances in molecular quantum chemistry contained in the Q-Chem 4 program package. Molecular Physics, Taylor & Francis, 2015, 113 (2), pp.184-215. <10.1080/00268976.2014.952696>. <hal-01389004>
  • Jie Liu, Valeria Vitelli, Enrico Zio, Redouane Seraoui. A Dynamic Weighted RBF-Based Ensemble for Prediction of Time Series Data from Nuclear Components. International Journal of Prognostics and Health Management, Prognostics and Health Management Society, 2015, pp.9. <hal-01176328>
  • Jie Liu, Redouane Seraoui, Valeria Vitelli, Enrico Zio. Nuclear power plant components condition monitoring by probabilistic support vector machine. Annals of Nuclear Energy, Elsevier Masson, 2013, 56, pp.23-33. <hal-00790421>

Communication dans un congrès5 documents

  • Jie Liu, Enrico Zio. Adaptive Support Vector Regression for Long-Term Prediction under Nonstationay Environments: Prognostics of Components in Nuclear Power Plants. 2015 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE 2015), Jul 2015, Beijing, China. <hal-01176337>
  • Jie Liu, Valeria Vitelli, Redouane Seraoui, Enrico Zio. Dynamic Weighted PSVR-Based Ensembles for Prognostics of Nuclear Components. FLINS 2014, Aug 2014, João Pessoa, Brazil. Proceedings of the 11th International FLINS Conference on Decision Making and Soft Computing. <hal-01108176>
  • Jie Liu, Valeria Vitelli, Redouane Seraoui, Enrico Zio. AN EFFICIENT ONLINE LEARNING APPROACH FOR SUPPORT VECTOR REGRESSION. second european conference of the prognostics and health management society 2014, Jul 2014, Nantes, France. <10.1142/9789814619998_0032>. <hal-01090273>
  • Jie Liu, Valeria Vitelli, Redouane Seraoui, Francesco Di Maio, Enrico Zio. Short-Term Prediction for Nuclear Power Plant Failure Scenarios Using an Ensemble-based Approach. ESREL 2013, Sep 2013, Amsterdam, Netherlands. pp.1-5, 2013. <hal-00838785>
  • Jie Liu, Redouane Seraoui, Valeria Vitelli, Enrico Zio. Probabilistic Support Vector Regression for Short-Term Prediction of Power Plants Equipment. Prognostics and System Health Management Conference - PHM-2013, Sep 2013, Milano, Italy. pp.1-6, 2013. <hal-00838776>

Pré-publication, Document de travail1 document

  • Jie Liu. Characterization of projective spaces and \mathbb{P}^r-bundles as ample divisors. 2016. <hal-01408052>