Miroslav Kubat

Photo of Miroslav Kubat

Miroslav Kubat

Associate Professor | College of Engineering | Electrical & Computer Engineering Department
Work Phone: (305) 284-3264


Resume
Intranet

HIGHER EDUCATION

    • Brno Technical University, Czech Republic, Ph.D. in Equivalent to German Habilitation. (1995)
    • Technical University of Brno, Ph.D. in Electrical and Computer Engineering. (1990)
    • Technical University of Brno, M.S. in Engineering and Technology. (1982)

EXPERIENCE

    • University of Miami, Associate Professor. Electrical and Computer Engineering (2001 - Present)
    • SABBATICAL: University of Ulm, Mendel University of Brno, . (2012)
    • University of Ulm, Germany, Visiting Professor. (2009)
    • University of Ulm, Germany, Visiting Professor. (2004)
    • University of Louisiana at Lafayette, Associate Professor. (1998 - 2001)
    • University of Ulm, Germany, Visiting Professor. (2000)
    • University of Ottawa, California, Senior Research Fellow. (1995 - 1997)
    • University of Ulm, Germany, Visiting Professor. (1995 - 1996)
    • Johannes Kepler University in Linz, Austria, Senior Research Fellow. (1994 - 1995)
    • Technical University in Graz, Austria, Lecturer and Research Fellow. (1992 - 1994)
    • Technical University of Brno, Czech Republic, Scientist. (1988 - 1992)

PUBLICATIONS

  • Juried or Refereed Journal Articles or Exhibitions

    • Nabizadeh, N., Kubat, M. (2017).Automated Tumor Segmentation in Single-Spectral MRI Using a Texture-Based and Contour-Based Algorithm. Expert Systems with Applications, 77, 1-10.
    • Martinez, O., Ranga, D., Kamal, P., Miroslav, K., James, E., Kubat, M. (2017).LFDA model for the assessment of water quality through Microtox using Excitation-Emission Matrices. Intelligent Data Analysis, 21, 181-203.
    • Nabizadeh, N., Kubat, M. (2015).Brain-tumors detection and segmentation in MR images: Gabor-Wavelets vs. Statistical Features. Computers and Electrical Engineering, 45, 286-301.
    • Alali, A., Kubat, M. (2015).PruDent: A Pruned and Confident Stacking Approach for Multi-label Classification. IEEE Transations on Data and Knowledge Engineering (accepted), 27, 2480-2493.
    • Martinez, O., Dababera, R., Premaratne, K., Kubat, M. (2015).LDA-Based Probabilistic Graphical Model for Excitation-Emission Matrices. Intelligent Data Analysis, 19, 1109-1130.
    • Vateekul, P., Kubat, M., Sarinnapakorn, K. (2014).Hierarchical Multi-Label Classification with SVMs: A Case Study in Gene Function Prediction. Intelligent Data Analysis, 18, 717-738.
    • Alsharif, H. H., Alhalabi, W. S., Kubat, M. (2014).Induction from Multi-Label Examples. Life Sciences, 11, 495--511.
    • Vateekul, P., Dendamrongvit, S., Kubat, M. (2013).Improving SVM Performance in Multi-Label Domains: Threshold Adjustment.. International Journal on Artificial Intelligence Tools, IJAIT, 22, [20 pages].
    • Wickramarathne, T., Premaratne, K., Kubat, M., Jayaweera, D. (2011).CoFiDS: A Belief Theoretic Approach for Automated Collaborative Filtering. IEEE Transactions on Knowledge and Data Engineering, 23, no. 2, 175-189.
    • Dendamrongvit, S., Kubat, M. (2011).Irrelevant Attributes and Imbalanced Classes in Multi-label Text-Categorization Domains. Intelligent Data Analysis, 15, 843-860.
    • Quirino, T., Kubat, M., Bryan, N. (2010).Instinct-Based Mating in Genetic Algorithms Applied to the Tuning of 1-NN Classifiers. IEEE Transactions on Knowledge and Data Engineering, 22, no.12, 1724-1737.
    • Wickramaratna, K., Kubat, M., Premaratne, K. (2009).Predicting Missing Items in Shopping Carts. IEEE Transactions on Data and Knowledge Engineering, 21, no.7, 985-998.
    • Wickramaratna, K., Kubat, M., Minnett, P. (2008).A Case Study on Numeric Law Discovery: CO_2 Fugacity in Sea Water. Intelligent Data Analysis, IOS Press, 12, no.4, 379-392.
    • Holland, H., Kubat, M., Zizka, J. (2008).Handling Ambiguous Attribute Values and Class Labels in Instance-Based Classifiers. International Journal on Artifical Intelligence Tools, 17, no.3, 449-469.
    • Sarinnapakorn, K., Kubat, M. (2008).Induction from Multilabel Examples in Information Retrieval Systems: A Case Study. Applied Artificial Intelligence, 22, no.3, 407-432.
    • Alhalabi, W., Kubat, M., Tapia, M. (2007).A Tool to Personalize the Ranking of the Documents Returned by an Internet Search Engine. Journal of Convergence Information Technology, vol. 2, no.3, 10-Jun.
    • Alhalabi, W., Kubat, M., Tapia, M. (2007).A Tool to Personalize the Ranking of the Documents Returned by an Internet Search Engine. Journal of Convergence Information Technology, 2, no.3, 10-Jun.
    • Sarinnapakorn, K., Kubat, M. (2007).Combining Subclassifiers in Text Classification: A DST-Based Solution and a Case Study. IEEE Transactions on Data and Knowledge Engineering, 19, no.12, 1638-1651.
    • Alhalabi, W., Kubat, M., Tapia, M. (2007).Search Engine Ranking Efficiency Evaluation Tool. SIGCSE Inroads Bulletin, ACM, 39, no.2, 97-102.
    • Yu, L., Kubat, M. (2006).Searching for High-Support Itemsets in Itemset Trees. Intelligent Data Analysis, 10, no.2, 105-120.
    • Rozsypal, A., Kubat, M. (2005).Association Mining in Time-Varying Domains. Intelligent Data Analysis, 9, no.3, 273-288.
    • Kubat, M., Hafez, A., Raghavan, V. V., Lekkala, J., Chen, W. K. (2003).Itemset Trees for Targeted Association Querying. IEEE Transactions on Data and Knowledge Engineering, 15, no.6, 1522-1534.
    • Rozsypal, A., Kubat, M. (2003).Selecting Representative Examples and Attributes by a Genetic Algorithm. Intelligent Data Analysis, 7, no.4, 291-304.
    • Kubat, M., Cooperson, Jr., M. (2001).A Reduction Technique for Nearest-Neighbor Classification: Small Groups of Examples. Intelligent Data Analysis, 5, no.6, 463-476.
    • Kubat, M. (2000).Designing Neural Network Architectures for Pattern Recognition. The Knowledge Engineering Review, 15, no.2, 151-170.
    • Kubat, M. (2000).Recycling Decision Trees in Numeric Domains. Informatica: An International Journal of Computing and Informatics, 24, no.2, 195-204.
    • Kubat, M., Furnkranz, J. (1999).Report on the Machine-Learning in Game-Playing Workshop. Journal of the International Computer Chess Association, ICCA, 22, no.3, 178-179.
    • Kubat, M. (1998).Decision Trees Can Initialize Radial-Basis-Function Networks. IEEE Transactions on Neural Networks, 9, no.5, 813-821.
    • Kubat, M., Holte, R., Matwin, S. (1998).Detection of Oil-Spills in Radar Images of Sea Surface. Machine Learning, 30, 195-215.
    • Widmer, G., Kubat, M. (1996).Learning in the Presence of Concept Drift and Hidden Contexts. Machine Learning, 23, no.1, 69-101.
    • Parsons, S., Kubat, M., Dohnal, M. (1995).A Rough Set Approach to Reasoning under Uncertainty. Journal of Experimental and Theoretical Artificial Intelligence, 7, no.2, 175-193.
    • Ivanova (Koprinska), I., Kubat, M. (1995).Initialization of Neural Networks by Means of Decision Trees. Knowledge-Based Systems, 8, no.6, 333-344.
    • Kubat, M., Flotzinger, D. (1995).Tree-like Structures of Linear Threshold Units for the Classification of Numeric Examples. Cybernetics and Systems, 26, no.5, 521-533.
    • Parsons, S., Kubat, M. (1994).A First Order Logic for Reasoning under Uncertainty Using Rough Sets. Journal of Intelligent Manufacturing, 5, no.4, 211-223.
    • Kubat, M., Pfurtscheller, G., Flotzinger, D. (1994).AI-Based Approach to Automatic Sleep Classification. Biological Cybernetics, 70, no.5, 443-448.
    • Kubat, M., Parsons, S. (1994).Approximating Knowledge in a Multi-Agent System. Informatica: An International Journal of Computing and Informatics, 18, no.2, 115-129.
    • Spacek, L., Kubat, M., Flotzinger, D. (1994).Face Recognition through Learned Boundary Characteristics. Applied Artificial Intelligence, 8, no.1, 149-164.
    • Kubat, M. (1993).Flexible Concept Learning in Real-Time Systems. Journal of Intelligent and Robotic Systems, 8, no.2, 155-171.
    • Kubat, M., Flotzinger, D., Pfurtscheller, G. (1993).Towards Automated Sleep Classification in Infants Using Symbolic and Subsymbolic Approaches. Biomedizinische Technik/ Biomedical Engineering, 38, no.4, 73-80.
    • Kubat, M. (1992).A Machine Learning Based Approach to Load Balancing in Computer Networks. Cybernetics and Systems, 23, no.3-4, 389-400.
    • Krizakova, I., Kubat, M. (1992).FAVORIT: Dynamic Approach to Concept Formation. Pattern Recognition Letters, 13, no.1, 19-25.
    • Kubat, M., Krizakova, I. (1992).Forgetting and Ageing of Knowledge in Concept Formation. Applied Artificial Intelligence, 6, no.2, 195-206.
    • Kubat, M. (1991).Conceptual Inductive Learning: The Case of Unreliable Teachers. Artificial Intelligence, 52, no.2, 169-182.
    • Kubat, M. (1989).Floating Approximation in Time-Varying Knowledge Bases. Pattern Recognition Letters, 10, no.4, 223-227.
  • Refereed Books

    • Kubat, M. (Ed.) Introduction to Machine Learning. Springer Publishers.
    • Halabi, W., Kubat, M. , Tapia, M. (Eds.) Induction-Based Approach to Personalized Search Engines. VDM Verlag.
    • Fuhrnkranz, J., Kubat, M. (Eds.) Machines that Learn to Play Games. NOVA Science Publishers.
    • Michalski, R. S., Bratko, I., Kubat, M. (Eds.) Machine Learning and Data Mining: Methods and Applications. Wiley.

    Book Chapters

    • Kubat, M., Sarinnapakorn, K., Dendamrongvit, S., (2010). Induction in Multi-Label Text Classification Domains. Advances in Machine Learning II (pp. 225-244). Springer Berlin / Heidelberg.
    • Subasingha, S. P., Zhang, J., Premaratne, K., Shyu, M., Kubat, M., Hewawasam, K., (2008). Using Association Rules for Classification from Databases Having Class Label Ambiguities: A Belief Theoretic Method. Data Mining: Foundations and Practice (pp. 539-562). Springer Berlin / Heidelberg.
    • Kubat, M., (2001). A Hyperrectangle-Based Method that Create RBF Networks. Radial-Basis Function Neural Network Design and Applications (pp. 31-50). Springer Verlag.
    • Kubat, M., (2001). Should Machines Learn How to Play Games?. Machines That Learn to Play Games (pp. 10-Jan). NOVA Science Publishers.
    • Kubat, M., Bratko, I., Michalski, R. S. (1998). A Review of Machine Learning Methods. Machine Learning and Data Mining: Methods and Applications (pp. Mar-69). Michalski, R.S., Kubat, M., and Bratko, I. (eds.), John Wiley & Sons.
    • Kubat, M., Koprinska, I., Pfurtscheller, G., (1998). Learning to Classify Biological Signals. Machine Learning and Data Mining: Methods and Applications (pp. 409-428). John Wiley & Sons.
    • Kubat, M., (1992). Introduction to Machine Learning. Advanced Topics in Artificial Intelligence (pp. 104-138). Springer Verlag, Lecture Notes in Computer Science 617, Berlin.

 

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