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Ioannis E. Livieris received his B.Sc., M.Sc. and Ph.D. degrees in Mathematics from the University of Patras, Greece in 2006, 2008 and 2012 respectively. His research interests include numerical optimization, neural networks and its application in bioinformatics. He is a member of the ESDLab since 2008. See his personal web page.
Degrees
- 2012: Ph.D. from Department of Mathematics, University of Patras.
- 2008: M.Sc. in "Computational Mathematics & Informatics", Department of Mathematics, University of Patras, Greece.
- 2006: Bachelor Degree in Mathematics (speciality in Computational Mathematics & Informatics), Department of Mathematics, University of Patras, Greece.
Dissertations
- Ph.D. Thesis: Nonlinear Conjugate Gradient Methods for Optimization and Neural Network Training. Supervisor: Professor P. Pintelas.
- M.Sc. Thesis: Performance Evaluation of Algorithms for Neural Network Training and Applications. Supevisor: Professor P. Pintelas.
- B.Sc. Thesis: Constraint Propagation Problems. Bachelor Thesis Supevisor: Associate Professor T.N. Grapsa.
Courses
E-mail :
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Phone : 2610 997833
Fax : 2610 997313
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Towards analyzing next generation sequencing algorithms. |
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A. Kanavos, I.E. Livieris, F. Mylonas, S. Sioutas and G. Vonitsanos. Towards analyzing next generation sequencing algorithms. In Advances in Experimental Medicine and Biology, Springer Verlag, 2018.
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DTCo: An ensemble SSL algorithm for X-rays classification |
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An improved self-labeled algorithm for cancer prediction |
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An ensemble-based semi-supervised approach for predicting students’ performance |
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DSS-PSP - A decision support software for evaluating students' performance |
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I.E. Livieris, K. Drakopoulou, Th. Kotsilieris, V. Tampakas and P. Pintelas. DSS-PSP - A decision support software for evaluating students’ performance. In 18th International Conference on Engineering Applications of Neural Networks (EANN 2017), 2017.
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A decision support system for predicting students’ performance |
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A limited memory descent Perry conjugate gradient method |
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A new class of nonmonotone conjugate gradient training algorithms |
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A modified Perry conjugate gradient method and its global convergence |
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A new conjugate gradient algorithm for training neural networks based on a modified secant equation |
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A new class of spectral conjugate gradient methods based on a modified secant equation for unconstrained optimization |
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Globally convergent modified Perry conjugate gradient method |
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An improved spectral conjugate gradient neural network training algorithm |
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I.E. Livieris and P. Pintelas, An Improved Spectral Conjugate Gradient Neural Network Training Algorithm, International Journal on Artificial Intelligence and Tools, 20(1), 2012.  
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A descent Dai-Liao conjugate gradient method based on a modified secant equation and its global convergence |
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An advanced conjugate gradient training algorithm based on a modified secant equation |
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Predicting students' performance using artificial neural networks |
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A memoryless BFGS Neural network training algorithm |
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M.S. Apostolopoulou, D.G. Sotiropoulos, I.E. Livieris and P. Pintelas, A Memoryless BFGS Neural Network Training Algorithm, In Proceedings of 6th IEEE International Conference on Industrial Informatics (INDIN 2009), p.p. 216-221, 2009. |
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Performance evaluation of descent CG methods for neural network training |
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I.E. Livieris and P. Pintelas, Performance Evaluation of Descent CG Methods for Neural Network Training, In Proceedings of The 9th Hellenic European Research on Computer Mathematics & Conference its Applications (HERCMA 2009), vol 11, pp. 40-46, Athens, 2009. |
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Classification of large biomedical data using ANNs based on BFGS method |
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I.E. Livieris, M.S. Apostolopoulou, D.G. Sotiropoulos, S.A. Sioutas and P. Pintelas, Classification of Large Biomedical Data using ANNs based on BFGS method. In Proceedings of 13th Panhellenic Conference on Informatics (PCI 2009), 2009 |
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On descent spectral CG algorithms for training recurrent neural networks |
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I.E. Livieris D.G. Sotiropoulos and P. Pintelas, On Descent Spectral CG Algorithms for Training Recurrent Neural Networks. In Proceedings of 13th Panhellenic Conference on Informatics (PCI 2009), 2009. |
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