Support Vector Machines, Neural Networks and Fuzzy Logic Models


V Kecman's Introduction to SVM: Report on the SVMs' Basics

Learning Algorithms:

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

SVM and Linear Programming:

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

NN Based Control and Identification:

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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