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
(M22) Rad u istaknutom međunarodnom časopisu
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
(M11) Istaknuta monografija međunarodnog značaja
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
(M11) Istaknuta monografija međunarodnog značaja
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
(M12) Monografija međunarodnog značaja
Kecman V., Process Dynamics, (Sc), 3rd Ed., Liber, Zagreb, YU, (300 p.), 1990
(M12) Monografija međunarodnog značaja
Kecman V., State-Space Models of Lumped and Distributed Systems, Springer-Verlag, Berlin, Heidelberg, New York, London, Paris, Tokio, (280 p.), 1988
(M22) Rad u istaknutom međunarodnom časopisu
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
(M22) Rad u istaknutom međunarodnom časopisu
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
(M22) Rad u istaknutom međunarodnom časopisu
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
(M22) Rad u istaknutom međunarodnom časopisu
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
(M22) Rad u istaknutom međunarodnom časopisu
Kecman V., Support Vector Machines for Pattern Classification, S. Abe, SIAM Review, Vol. 48, No. 2, pp. 418 – 421, 2006
(M22) Rad u istaknutom međunarodnom časopisu
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
(M22) Rad u istaknutom međunarodnom časopisu
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
(M42) Monografija nacionalnog značaja, monografsko izdanje građe,
Kecman V., Foundations of Automatic Control, (Sc), Zagreb, YU, (253 p.), 1988
(M42) Monografija nacionalnog značaja, monografsko izdanje građe,
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
