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
(М22) Рад у истакнутом међународном часопису
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
(М11) Истакнута монографија међународног значаја
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, see http://www.support-vector.ws
(М11) Истакнута монографија међународног значаја
Kecman V., Learning and Soft Computing, Support Vector Machines, Neural Networks, and Fuzzy Logic Models, The MIT Press, Cambridge, MA, USA, (608 p.), 2001, see http://www.support-vector.ws
(М12) Монографија међународног значаја
Kecman V., Process Dynamics, (Sc), 3rd Ed., Liber, Zagreb, YU, (300 p.), 1990
(М12) Монографија међународног значаја
Kecman V., State-Space Models of Lumped and Distributed Systems, Springer-Verlag, Berlin, Heidelberg, New York, London, Paris, Tokio, (280 p.), 1988
(М22) Рад у истакнутом међународном часопису
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
(М22) Рад у истакнутом међународном часопису
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
(М22) Рад у истакнутом међународном часопису
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
(М22) Рад у истакнутом међународном часопису
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
(М22) Рад у истакнутом међународном часопису
Kecman V., Support Vector Machines for Pattern Classification, S. Abe, SIAM Review, Vol. 48, No. 2, pp. 418 – 421, 2006
(М22) Рад у истакнутом међународном часопису
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
(М22) Рад у истакнутом међународном часопису
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., V. Kecman, I. Kopriva, Kernel Based Algorithms for Mining Huge Data Sets, Supervised, Semi-supervised, and Unsupervised Learning, Springer-Verlag, Berlin, Heidelberg, 2006, see http://www.learning-from-data.com
