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Faculty and Staff Directory

Vojislav Kecman, Ph.D.

Vojislav Kecman, Ph.D.

Professor, Department of Computer Science
Email: vkecman@vcu.edu
Website: www.people.vcu.edu/~vkecman/

Address
Virginia Commonwealth University
School of Engineering
Department of Computer Science
East Hall, Room E4250
401 West Main Street
P.O. Box 843019
Richmond, Virginia 23284-3019

Education

  • Dipl. Ing., University of Zagreb, 1972
  • M.Sc., University of Zagreb, 1978
  • Ph.D., University of Zagreb, 1982

Publications

  • Gajic Z., M. Lim, D. Skataric, W. Su, V. Kecman, Optimal Control of Weakly Coupled Systems and Applications, CRC Press (Francis and Taylor), Book, 2009.
  • Yang T., Kecman V., Adaptive Local Hyperplane Classification, Neurocomputing 71, pp. 3001-3004, 2008.
  • Yang T., Kecman V., Face recognition with adaptive local hyperplane algorithm, Pattern Analysis & Applications, Springer-Verlag, pp. , 2008.
  • Murphy R.B., Young B.R., Kecman V., Optimising operation of a biological wastewater treatment application, ISA Transactions 48, pp. 93-97, 2008.
  • Yang T., Kecman V., A novel algorithm for learning small medical dataset, Expert Systems, in print, 2008.
  • Yang, T., Kecman, V., Classification by ALH-Fast algorithm. Softcomputing, in print, 2008.
  • Johnny Wei-Hsun Kao, Stevan Berber, Vojislav Kecman, Blind Multiuser Detection of a Chaos-based CDMA System using Support Vector Machines, 10th International Symposium on Spread Spectrum Techniques and Applications, Proceedings, Bologna, Italy, 2008.
  • Guocai Chen, Jim Warren, Tao Yang and Vojislav Kecman, Adaptive K-Local Hyperplane (AKLH) Classifiers on Semantic Spaces to Determine Health Consumer Webpage Metadata, Proceedings of The 21th IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS 2008), Jyväskylä, Finland, pp. 287-289, 2008.
  • Leonhardt S., Kecman V., Novel Features for Automated Lung Function Diagnosis in Spontaneously Breathing Infants, in ‘Artificial Intelligence in Medicine’, LNAI 4594, Eds. Bellazzi R., Abu-Hanna A., Hunter J., Springer-Verlag, Berlin, Heidelberg, pp. 195-199, 2007.
  • Kim T.S., Stol K., Kecman V., Control of 3 DOF Quadrotor Model, in Robot Motion and Control 2007, Lecture Notes in Control and Information Sciences, Vol. 360, Springer-Verlag, London, pp. 29-39, 2007.
  • Kecman V., High Dimensional Function Approximation (Regression, Hypersurface Fitting) by an Active Set Least Squares Learning Algorithm, School of Engineering Report 643, The University of Auckland, Auckland, NZ, (53 p.), 2006.
  • Huang T.-M., V. Kecman, I. Kopriva, Kernel Based Algorithms for Mining Huge Data Sets, Supervised, Semi-supervised, and Unsupervised Learning, Springer-Verlag, Berlin, Heidelberg, 2006.
  • Kecman V., Support Vector Machines for Pattern Classification, S. Abe, SIAM Review, Vol. 48, No. 2, pp. 418 – 421, 2006.
  • Kecman V., Tomasevic M., Eigenvector Approach for Reduced-Order Optimal Control Problems of Weakly Coupled Systems, Dynamics of Continuous, Discrete and Impulsive Systems: An International Journal for Theory and Applications (DCDIS), B: Applications and Algorithms, Volume 13, Number 5, pp. 569-587, 2006.
  • Huang T.-M., Kecman V., Semi-supervised Learning from Unbalanced Labeled Data – An Improvement, International Journal of Knowledge-Based and Intelligent Engineering Systems, Special Issue: Innovational Soft Computing, IOS Press, Vol 10., No. 1, pp. 21 – 27, 2006.
  • Kecman V., New Support Vector Machines Algorithm for Huge Data Sets, 8th All-Russian Scientific Conference on Neural Networks, Neiroinformatika, Conference Plenary Lecture, Jan 24 – 27, 2006, Moscow, Russia, 2006.
  • Huang, T.-M., Kecman, V., Gene Extraction for Cancer Diagnosis by Support Vector Machines, in Lecture Notes in Computer Science, Eds. W. Duch, J. Kacprzyk, E. Oja, et al., Volume 3696, pp. 617 ? 624, Springer-Verlag, 2005.
  • Huang, T.-M., Kecman, V., Performance Comparisons of Semi-Supervised Learning Algorithms, Proceedings of the Workshop on Learning with Partially Classified Training Data, at the 22nd International Conference on Machine learning, ICML 2005, W5, pp. 45-49, Bonn, Germany, 2005.
  • Kecman V., Learning and Soft Computing, Support Vector Machines, Neural Networks, and Fuzzy Logic Models, Pearson Education India, (Special Indian Edition), New Delhi, India, 2005.
  • Huang T.-M., Kecman V., Gene extraction for cancer diagnosis by support vector machines – An improvement, Artificial Intelligence in Medicine (2005) 35, pp. 185-194, Special Issue on Computational Intelligence Techniques in Bioinformatics, 2005.
  • Kecman V., Chapter “Basics of Machine Learning by Support Vector Machines”, in a Springer-Verlag book, “Real World Applications of Computational Intelligence”, Series: Studies in Fuzziness and Soft Computing, Vol. 179, pp. 49-103, Eds. M. Negoita , B. Reusch, 2005.
  • Kecman V., Chapter “Support Vector Machines – An Introduction”, in a Springer-Verlag book, “Support Vector Machines: Theory and Applications”, Ed. L. Wang, Series: Studies in Fuzziness and Soft Computing, Vol. 177, pp. 1-47, 2005.
  • Vogt M., V. Kecman, Chapter “Active-Set Methods for Support Vector Machines”, in a Springer-Verlag book, “Support Vector Machines: Theory and Applications”, Ed. L. Wang, Series: Studies in Fuzziness and Soft Computing, Vol. 177, pp. 133-158, 2005.
  • Kecman V., T.-M. Huang, M. Vogt, Chapter “Iterative Single Data Algorithm for Training Kernel Machines from Huge Data Sets: Theory and Performance”, in a Springer-Verlag book, “Support Vector Machines: Theory and Applications”, Ed. L. Wang, Series: Studies in Fuzziness and Soft Computing, Vol. 177, pp. 255-274, 2005.
  • Kecman V., J. Robinson, Method, Apparatus and Software for Lossy Data Compression and Function Approximation, Patent, 2004.
  • Kecman V., Support Vector Machines Basics, School of Engineering Report 616, The University of Auckland, Auckland, NZ, (58 p.), 2004.
  • Huang, T.-M., Kecman, V., Semi-supervised Learning from Unbalanced Labeled Data – An Improvement, in ‘Knowledge Based and Emergent Technologies Relied Intelligent Information and Engineering Systems’, Eds. Negoita, M. Gh., et al., Lecture Notes on Computer Science 3215, pp. 765-771, Springer Verlag, Heidelberg, 2004.
  • Huang, T.-M., Kecman, V., Gene Extraction for Cancer Diagnosis by Support Vector Machines, Proceedings of International Conference on Bioinformatics (InCoB), Sept. 5-8, Auckland, 2004.
  • Vogt, M., Kecman, V., An Active-Set Algorithm for Support Vector Machines in Nonlinear System Identification, Proceedings of the 6th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2004), pp. 495-500, Stuttgart, Germany, 2004.
  • Huang T.?M., Kecman V., Bias Term b in SVMs Again, Proc. of the 12th European Symposium on Artificial Neural Networks, ESANN 2004, pp. 441-448, Bruges, Belgium, 2004.
  • Abdulla W., Kecman V., Kasabov N., Speech-Background Classification by Using SVM Technique, 13th International Conference on Artificial Neural Networks, ICANN/ICONIPP 2003, June 26-29, Istanbul, Turkey, 2003.
  • Kecman V., Vogt M., Huang T-M., On the Equality of Kernel AdaTron and Sequential Minimal Optimization in Classification and Regression Tasks and Alike Algorithms for Kernel Machines, Proc. of the 11th European Symposium on Artificial Neural Networks, ESANN 2003, pp. 215 ? 222, Bruges, Belgium, 2003.
  • Vojinovic Z., Kecman V., Data Assimilation Using Radial Basis Function Neural Network Model, Proceedings of International Symposium on Computational Intelligence for Measurement and application, pp. 61-66, Lugano, Switzerland, 2003.
  • Vogt M., Spreitzer K., Kecman V., Identification of a high efficiency boiler by support vector machines without bias term, Preprints of the 13th IFAC Symposium on System Identification (SYSID 2003), pp. 485-490, Rotterdam, The Netherlands, 2003.
  • Robinson J., Kecman V., Combining Support Vector Machine Learning with the Discrete Cosine Transform in Image Compression, IEEE Transactions on Neural Networks, Vol. 14, No. 4, pp. 950-958, July 2003.
  • Vojinovic Z., Kecman V., Babovic V., Hybrid Approach for Modeling Wet Weather Response in Wastewater Systems, Journal of Water Resources Planning and Management, ASCE, Vol. 129, Issue 6, pp. 511-521 2003.
  • Lin J.T, Bhattacharyyaa D., Kecman V., Multiple regression and neural networks analyses in composites machining, Composites Science and Technology, 63, No.3, pp.539-548, 2003.
  • Li Z. Q., Kecman V., Ichikawa A., Fuzzified Neural Network Based on Fuzzy Number Operations, Fuzzy Sets and Systems 130, No. 3, pp. 291-304, 2002.
  • Kecman V., Z. Q. Li, Fuzzy calculus by RBF Neural Networks, Proceedings of the Sixth International Conference on Neural Networks and Soft Computing ICNNSC 2002, Zakopane, Poland, June 11-15, Springer-Verlag, pp. 516-522, 2002.
  • Kecman V., Learning and Soft Computing, Support Vector Machines, Neural Networks, and Fuzzy Logic Models, The MIT Press, Cambridge, MA, USA, (608 p.), 2001.
  • Kecman V., Arthanari T., Hadzic I, LP and QP Based Learning From Empirical Data, IEEE Proceedings of IJCNN 2001, Vol 4., pp., 2451-2455, Washington, DC, 2001.
  • Kecman V., Hadzic I., Support Vectors Selection by Linear Programming, Proceedings of the International Joint Conference on Neural Networks (IJCNN 2000), Vol. 5, pp. 193-198, Como, Italy, 2000.