Prof.
Theodore B. Trafalis
The
University of Oklahoma, USA
Email: ttrafalis@ou.edu
Qualifications
1989
Ph.D, Purdue University, USA
1987
M.S.I.E., Purdue University, USA
1984
M.S., Mathematics, Purdue University, USA
1982
B.S., Mathematics, University of Athens, Greece
Publications
(selected)
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Trafalis,
T.B., O.O. Oladunni and M. Richman, “Linear Classification Tikhonov
Regularization Knowledge-based Support Vector Machine for Tornado Forecasting,”
submitted to Computational Management Science, in press, 2010.
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Adrianto,
I. and T.B. Trafalis, “The p-center Machine for Regression analysis”,
Optimization Methods and Software, accepted, 2009.
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Gilbert R.
C., T.B. Trafalis, M. B. Richman and S. Lakshmivarahan, “Real-time Prediction
Using Kernel Methods and Data Assimilation”, series in Intelligent Engineering
Systems Through Artificial Neural Networks, Computational Intelligence in
Architecturing Complex Engineering Systems, pp. 35-42.ASME Press, New York, NY,
USA, 2009.
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Oladunni,
O.O. and T.B. Trafalis, “A regularized Pairwise Multi-classification
Knowledge-based Machine and Applications”, European Journal of Operational
Research, 195(3):924-941, 2009.
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Trafalis,
T.B. and S. Kasap, “Neural Networks for Combinatorial Optimization”, in
Encyclopedia of Optimization, (C.A. Floudas and P.M. Pardalos, editors), second
edition, pp. 2547-2555, Springer, New York, NY, USA, 2009.
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Gilbert,
R.C., S. Raman, T.B. Trafalis, S.M. Obeidat, and J.A. Aguirre-Cruz,
“Mathematical Foundations for Form Inspection and Adaptive Sampling”, Journal
of Manufacturing Science and Engineering, 2009. Accepted, in press.
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Adrianto,
I, T.B. Trafalis and M.B. Richman, “Active Learning with Kernel Machines for
Tornado Detection”, in Intelligent Engineering Systems Through Artificial
Neural Networks, 8:131-137, New York, NY, USA, 2008, ASME Press.
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Maalouf,
M. and T.B. Trafalis, “Kernel Logistic Regression using Truncated Newton
Method”, in Intelligent Engineering Systems Through Artificial Neural Networks,
(C.H. Dagli, D.L. Enke, K.M. Bryden, Y. Ceylan and M. Gen, editors),
18:455-462, New York, NY, USA, ASME Press, 2008.
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Trafalis,
T.B. and R.C. Gilbert, “Nonlinear Programming”, in Operations Research and
Management Science Handbook, (A.R. Ravindran, editor), CRC pp. 2-1 to 2-22,
2008.
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Mansouri,
H., M.B. Richman, T.B. Trafalis, and L.M. Leslie, “ Pipeline Support Vector
Regression Method to Thin large Ocean Surface Wind Data On-line”, in
Intelligent Engineering Systems Through Artificial Neural Networks, (C.H.
Dagli, D.L. Enke, K.M. Bryden, H. Ceylan, and M. Gen, editors), 18:203-210, New
York, NY,USA, 2008, ASME Press.
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Richman,
M.B., T.B. Trafalis and I. Adrianto, “Missing Data Imputation through Machine
Learning Algorithms”, in Artificial Intelligence Methods in the Environmental
Sciences, (S.E. Haupt, A. Pasini and C. Marzban, editors), pp 153-169,
Springer, London, UK, 2008.
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Oladunni,
O.O. and T.B. Trafalis, “A Nonlinear Multi-classification Knowledge-based
Kernel Machine”, Computational Management Science, 2008. Available
online.
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Kundakcioglu,
O.E., M. Sanguineti and T.B. Trafalis, Guest editorial, Computational
Management Science, 2008. Available online.
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Maalouf,
M., N. Khoury and T.B. Trafalis, “Support Vector Regression to Predict Asphalt
Mix Performance”, International Journal for Numerical and Analytical Methods in
Geomechanics, 32(16):1989-1996, 2008.
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Balakrishna,
P., S. Raman, T.B. Trafalis and B. Santosa, “Support Vector Regression for
Determining the Minimum Zone Sphericity”, International Journal of Advanced
Manufacturing Technology, 35(9-10):916-923, 2008.
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Alenezi,
A., S.A. Moses, and T.B. Trafalis, “Real-time Prediction of Order Flowtimes
using Support Vector Regression, Computers and Operations Research,
35(11):3489-3503, 2008.
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Papadakis,
P., I. Pratikakis, T.B. Trafalis, T. Theoharis and S. Perantonis, “Relevance
Feedback in Content-based 3D Object Retrieval – A Comparative Study,” Computer
Aided Design and Applications (CAD&A), 5(5):753-763, 2008.
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Oladunni,
O.O. and T.B. Trafalis, “A Regularized Pairwise Multi-classification
Knowledge-based Machine and Applications,” European Journal of Operational
Research, pp. 643-689, 2008.
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Adrianto,
I., Trafalis, T. B., & Lakshmanan, V., “Support vector machines for
spatiotemporal tornado prediction”, International Journal of General Systems,
Volume 38, Issue 7, Pages 759 – 776, 2009.
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Trafalis,
T. B. and R. C. Gilbert. “Nonlinear Programming,” in Operations Research and
Management Science Handbook, A. R. Ravindran, editor, CRC pp. 2-1 to 2-22,
2008.
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Ince, H.
and T.B. Trafalis, “Short term Forecasting with Support Vector Machines and Application
to Stock Price prediction”, International Journal of General Systems,
37(6):677-687, 2008.
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Trafalis,
T.B. and R. C. Gilbert, “Robust Support Vector Machines for Classification and
Computational Issues”, Optimization Methods and Software, 22(1):187-198, 2007.
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Trafalis,
T.B. and O.O. Oladunni, “Support Vector Machines and Applications,” invited
book chapter in Data Mining of Enterprise Data, Springer, T. Warren Liao and E.
Triantaphyllou, eds., World Scientific, 14:643-690, 2007.
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Oladunni,
O.O. and T.B. Trafalis, “Regularized Knowledge-based Kernel Machine,” ICCS
2007, Lecture notes in Computer Science, (Y. Shi et al., eds.), Part I, LNCS
4487, Springer-Verlag Berlin Heidelberg, pp.176-183, 2007.
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Santosa,
B. and T.B. Trafalis, “Robust Multiclass Kernel-based Classifiers”,
Computational Optimization, 38(2):261-280. 2007.
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Oladunni,
O.O. and T.B. Trafalis, “Regularization based Classification Models,”
Proceedings of the International Joint Conference on Neural (IJCNN’07), IEEE
Press, pp. 25 – 30, 2007, on CDROM.
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Ince, H.
and T.B. Trafalis, “Kernel Principal Component Analysis and Support Vector
Machines for Stock Price Prediction”, special issue of the IIE Transactions on
Quality and Reliability, 39(6):629-637, 2007.
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T.B.
Trafalis and S. Alwazzi, “Support vector regression with noisy data: A second
order cone programming approach”, International Journal of General Systems,
36(2):237-250, 2007.
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Santosa,
B., T. Conway and T.B. Trafalis, “A Hybrid Knowledge Base-Clustering
Multi-Class SVM for Genes Expression Analysis”, Data Mining in Biomedicine,
P.M. Pardalos, V. Boginski and A. Vazacopoulos (eds.) Springer, pp. 261-274 ,
February 2007.
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Alenezi,
A., S. A. Moses and T. B. Trafalis, “Real-Time Prediction of Order Flowtimes
Using Support Vector Regression”, special issue of Computers & Operations
Research on real-time supply chain management, 35(11):3489-3503, available
online 12 February 2007.
Profile
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