Knowledge-based multiclass support vector machines applied to vertical two-phase flow

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3991 LNCS - I, Issue , 2006, Pages 188-195

  Oladunni, Olutayo O ^a   Trafalis, Theodore B ^a   Papavassiliou, Dimitrios V ^a  

^a University of Oklahoma   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA REDUCTION; ITERATIVE METHODS; PROBLEM SOLVING; TRANSLATION (LANGUAGES); TWO PHASE FLOW; VECTORS;

MULTI CLASSIFICATION MODEL; REGULARIZATION PROBLEM; TRANSITION EQUATIONS; VECTOR MACHINES;

KNOWLEDGE BASED SYSTEMS;

EID: 33746587742     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11758501_29     Document Type: Conference Paper

References (18)

1. McQuillan, K.W., Whalley, P.B.: Flow Patterns in Vertical Two-Phase Flow. Int. J. Multiphase Flow, (1985) 11, 161-175
2. Mandhane, J.M., Gregory, G.A., Aziz, K.: A Flow Pattern Map for Gas-Liquid Flow in Horizontal Pipes. Int. J. Multiphase Flow, (1974) 1, 537-553
3. Taitel, Y., Bornea, D., Dukler, A.E.: Modeling Flow Pattern Transitions for Steady Upward Gas-Liquid Flow in Vertical Tubes. AIChE J., (1980) 26, 345
4. Taitel, Y., Dukler, A.E.: A Model for Predicting Flow Regime Transitions in Horizontal and Near Horizontal Gas-Liquid Flow. AIChE J, (1976) 22, 47
5. Petalas, N., Aziz, K.: A Mechanistic Model for Multiphase Flow in Pipes. CIM98-39, Proceedings, 49th Annual Technical Meeting of the Petroleum Society of the CIM, Calgary, Alberta, Canada, (1998) June 8-10
6. Osman, E.A.: Artificial Neural Networks Models for Identifying Flow Regimes and Predicting Liquid Holdup in Horizontal Multiphase Flow. SPE 68219, (2000) March
7. Mi, Y., Ishii, M., Tsoukalas, L.H.: Flow Regime Identification Methodology with Neural Networks and Two-Phase Row Models. Nuclear Engineering and Design, (2001), 204, 87-100
8. Ternyik, J., Bilgesu, H.I., Mohaghegh, S.: Virtual Measurement in Pipes, Part 2: Liquid Holdup and Row Pattern Correlations. SPE 30976, (1995) September
9. Trafalis, T.B., Oladunni, O., Papavassiliou, D.V.: Two-Phase Flow Regime Identification with a Multi-Classification SVM Model. Industrial & Engineering Chemistry Research, (2005) 44, 4414-4426
10. Mangasarian, O.L., Shavlik, J.W., Wild, E.W.: Knowledge-Based kernel approximation. Journal of Machine Learning Research, 5, 1127-1141, (2004)
11. Fung, G., Mangasarian, O.L., Shavlik, J.W.: Knowledge-Based support vector machine classifiers. Neural Information Processing Systems 2002 (NIPS 2002), Vancouver, BC, December 10-12, (2002). "Neural Information Processing Systems 15", S. Becker, S. Thrun and K. Obermayer, editors, MIT Press, Cambridge, MA, 2003, 521-528
12. Tikhonov, A.N., Arsenin, V.Y.: Solution of III-Posed Problems. Winston, Washington D.C., (1977)
13. Pelckmans, K., Suykens, J.A.K., De Moor, B.: Morozov, Ivanov and Tikhonov regularization based LS-SVMs. In Proceedings of the International Conference On Neural Information Processing (ICONIP 2004), Calcutta, India, Nov. 22-25, (2004)
14. Oladunni, O., Trafalis, T.B.: Linear Multi-classification Tikhonov Regularization Knowledge-based Support Vector Machine (LMT RKSVM), Technical Report, School of Industrial Engineering, University of Oklahoma, Norman, Oklahoma (2005)
15. Santosa, B., Conway, T., Trafalis, T.B.: Knowledge Based-Clustering and Application of Multi-Class SVM for Genes Expression Analysis. Intelligent Engineering Systems through Artificial Neural Networks, (2002), 12, 391-395
16. Hsu, C-W., Lin, C-J.: A Comparison of Methods for Multi-class Support Vector Machines. IEEE Transactions on Neural Networks, (2002) 13, 415-425
17. Lewis, J. M., Lakshmivarahan, S., Dhall, S.: Dynamic Data Assimilation. Cambridge University Press, (2006)
18. MATLAB User's Guide. The Math-Works, Inc., Natwick, MA 01760, (1994-2003). http://www.mathworks.com