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Supervised
Learning Algorithms:
Kecman,
V., Huang T.-M., Vogt M., Iterative Single Data Algorithm for Training
Kernel Machines from Huge Data Sets: Theory and Performance, Chapter in
'Support Vector Machines: Theory and Applications, Ed. Wang, L., Series:
Studies in Fuzziness and Soft Computing, Springer Verlag, Vol. 177,
pp.255-274, 2005.
Huang,
T.-M., Kecman, V., Gene
Extraction for Cancer Diagnosis using 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., Gene Extraction for Cancer
Diagnosis using Support Vector Machines: An improvement and comparison with
nearest shrunken centroid method.
Lecture Notes in Computer Science 3696, pp. 617-624, 2005.
Huang,
T.-M. Kecman. V., Bias Term b in SVMs Again, 12th European Symposium on Artificial
Neural Network, ESANN 2004, pp. 441-448, Bruges, Belgium, April 28-30, 2004.
Kecman,
V., Vogt, M., Huang,
T.-M.,
On the Equality
of Kernel AdaTron and Sequential Minimal Optimization in Classification and
Regression Tasks and Alike Algorithm for Kernel Machines, 11th European
Symposium on Artificial Neural Networks, ESANN 2003, pp. 215-222, Bruges,
Belgium, April 23-25, 2003.
Semi-Supervised
Learning Algorithms:
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.
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, Germany, 2005.
Huang,
T.-M., Kecman, V., Semi-supervised Learning from Unbalanced Labeled Data
– An Improvement, 'Knowledge Based and Emergent Technologies Relied
Intelligent Information and Engineering Systems', Eds. Negoita, M. Gh., at
al., Lecture Notes in Computer Science 3215, pp. 765-771, Springer Verlag,
Heidelberg, 2004. Best Paper Award and Best Student Contribution Paper
Award.
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Unsupervised
Learning Algorithms:
Du,
Q.,
Kopriva, I.
and
Szu, H., Independent
Component Analysis for Hyperspectral Remote Sensing, Optical
Engineering, vol. 45, 017008, January 2006.
Kopriva,
I., Single
Frame Multichannel Blind Deconvolution by Non-negative Matrix
Factorization with Sparseness Constraint , Optics Letters,
Vol. 30, No. 23, pp. 3135-3137,
December 1st,
2005
.
Szu,
H.,
Kopriva, I.,
Unsupervised
Learning with Stochastic Gradient, Neurocomputing,
Vol. 68 pp. 130-160, 2005.
Du,
Q., Kopriva, I. and Szu, H., Independent
Component Analysis for Classifying Multispectral Images with
Dimensionality Limitation, International Journal of
Information Acquisition, vol. 1, no. 3, pp.201-216, September
2004.
Kopriva,
I., Du, Q., Szu, H. and Wasylkiwskyj, W., Independent
Component Analysis Approach to Image Sharpening in the Presence of
Atmospheric Turbulence, Optics Communications, Vol. 233
(1-3) pp. 7-14, 2004.
Kopriva,
I., Szu, H.H., Persin, A., Optical
Reticle Trackers with Multi-Source Dicrimination Capability By
Using Independent Component Analysis, Optics
Communications, Vol. 203 (3-6) pp. 197-211, 2002.
Szu,
H. H., Kopriva,
I., Artificial
Neural Networks for Noisy Image Super-resolution, Optics
Communications, Vol. 198 (1-3) pp. 71-81, 2001.
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