Report on the SVMs' Basics
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., 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
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., at al., Lecture
Notes on Computer Science 3215, pp. 765-771, Springer Verlag,
Heidelberg, 2004
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, 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, 2005
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, Proceedings
of the ESANN 2003, 11th European Symposium on Artificial Neural
Networks, Bruges, Belgium,
April 23-25, 2003
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
Hadzic I., Kecman V., Support Vector
Machines Trained by Linear Programming. Theory and Application
in Image Compression and Data Classification, Proceedings
of Neurel 2000, IEEE Fifth Seminar on Neural Network Applications
in Electrical Engineering, pp 18-23, Beograd, Yugoslavia,
2000
Robinson J., Kecman V., Combining Support Vector Machine Learning with the Discrete Co-sine Transform in Image Compression, IEEE Transactions on Neural Networks, Vol. 14, No. 4, pp. 950-958, July 2003
Kecman V., Vlacic Lj., Salman R., Learning
in and performance of the new neural network based adaptive
backthrough control structure, Proceedings of the
14th IFAC Triennial World Congress, Beijing, PR China,
Vol. K, pp. 133-140, Pergamon, 1999
Kecman V., Learning in Adaptive Backthrough
Control Structure, The IEEE Third International
Conference on Algorithms And Architectures for Parallel
Processing (ICA3PP-97), Proc., Melbourne, Australia pp.
611-624, World Scientific, Singapore, 1997
Kecman V., System Identification Using
Modular Neural Network With Improved Learning,
The International Workshop on Neural Networks for Identification,
Control, Robotics, and Signal/Image Processing, NICROSP’ 96,
Venice, pp. 40-48, IEEE Computer Society Press 1996
Swider D.J., Browne M.W., Bansal P.K., Kecman V., Modelling
of Vapour-Compression Liquid Chillers With Neural Networks,
Journal of Applied Thermal Engineering, Pergamon Press,
Volume 21, Issue 3, pp. 311-329, February 2001
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