Laboratorij za kognitivno modeliranje

Laboratorij za kognitivno modeliranje (LKM) je bil ustanovljen decembra leta 2001. V Laboratoriju se raziskuje na področjih kognitivnega modeliranja, strojnega učenja, nevronskih mrež, odkrivanja znanja v podatkih in v slikah. Raziskovalna podpodročja, s katerimi se intenzivno ukvarjamo, so modeliranje iz šumnih podatkov, ki je povezano s kognitivnimi, medicinskimi, biološkimi in drugimi procesi. Razvijamo, testiramo in apliciramo nove pristope in algoritme za modeliranje iz numeričnih, simboličnih in slikovnih podatkov. Laboratorij za kognitivno modeliranje sodeluje s psihologi, zdravniki, biologi, fiziki in kemiki. Pomemben del raziskav je povezan z analizo slik, medicinsko diagnostiko, raziskavami vplivov različnih polj na vitalnost rastlin in ljudi.

Kje smo?

Laboratorij za kognitivno modeliranje ima prostore v podpritličju avle (iz avle navzdol po stopnicah mimo avtomatov za kavo, poleg predavalnice PR04). Drugi del prostorov LKM je na Jadranski 21 (soba 2), kjer imata kabinet doc. dr. Robnik in doc. dr. Kukar.

Sodelavci

• prof. dr. Igor Kononenko, Vodja laboratorija
• doc. dr. Matjaž Kukar
• izr. prof. dr. Marko Robnik-Šikonja
• doc. dr. Zoran Bosnić
• as. mag. Petar Vračar
• Erik Štrumbelj
• uni. dipl. ing Darko Pevec
• as. Matej Pičulin
• Miha Drole

Izbrane objave

·   M. Bevk, I.Kononenko (2006) Towards symbolic mining of images with association rules: Preliminary results on textures. Intelligent data analysis, pp. 379-393

·   D. Cukjati, M. Robnik-Šikonja, S. Reberšek, I. Kononenko, D. Miklavčič. Prognostic factors, prediction of chronic wound healing and electrical stimulation. Medical & Biological Engineering & Computing, 39, 542–550, 2001.

·   I.Kononenko, N.Lavrač: Prolog Through Examples: A Practical Programming Guide, Sigma Press, 1988.

·   Kononenko. Bayesian Neural Networks, Biological Cybernetics Journal, 61:361–370, 1989.

·   Kononenko, I. Bratko. Information based evaluation criterion for classi­fier’s performance. Machine Learning Journal, 6:67–80, 1991.

·   Kononenko. Inductive and Bayesian learning in medical diagnosis. Applied Artificial Intelligence, 7:317–337, 1993.

·   Kononenko, S.J. Hong. Attribute selection for modeling, Future Generation Computer Systems – ISSN 0167-739X, 13(2–3):181–195, 1997.

·   Kononenko, E. Šimec, M. Robnik. Overcoming the myopia of inductive learning algorithms, Applied Intelligence, 7:39–55, 1997.

·   Kononenko (ed.) Information Society 2000: New Science of Consciousness – Proc. 3rd Int. Conf. on Cognitive science – ISBN 961-6303-27-9 (Ljubljana, 17–19 October 2000), Institut Jožef Stefan, 129 pages.

·   Kononenko: Machine learning for medical diagnosis: History, state of the art and perspective, Invited paper, Artificial Intelligence in Medicine – ISSN 0933-3657, 23(1):89–109, 2001.

·   Kononenko, I. Jerman (eds.). Mind-body studies : proceedings of 6th International Conference on Cognitive Science, Ljubljana, 13-17th October 2003. Ljubljana: Institut “Jožef Stefan”, 190 pages.

·   I.Kononenko: Strojno učenje, 2. popravljena in dopolnjena izdaja, - ISBN 961-6209-52-3, FRI, Ljubljana, 2005.

·   Kononenko, M.Kukar: Machine Learning and Data Mining: Introduction to Principles and Algorithms, Horwood publ., 2006.

·   M. Kukar, N. Bešič, I. Kononenko, M. Aursperg, M. Robnik-Šikonja. Prognosing the survival time of patients with the anaplastic thyroid carcinoma using machine learning, In: N. Lavrač et al. (eds) Intelligent data analysis in medicine and Pharmacology – ISBN 0-7923-8000-2. Kluwer Academic Publ., 1997.

·   M. Kukar, I. Kononenko. Cost-sensitive learning with neural networks, Proc. European conf. on Arificial Intteligence ECAI-98 – ISBN 0-471-98431-0, Brighton, August 1998, pp. 445–449.

·   M. Kukar, I. Kononenko, C. Grošelj, K. Kralj, J. Fettich. Analysing and improving the diagnosis of ischaemic heart disease with machine learn­ing, Artificial Intelligence in Medicine, 16:25–50, 1999.

·   M. Kukar, I. Kononenko, T. Silvester. Machine learning in prognostics of the femoral neck fracture recovery, Artificial intelligence in medicine – ISSN 0933-3657, 8:431–451, 1996.

·   N. Lavrač, I. Kononenko, E. Keravnou, M. Kukar, B. Zupan: Intelligent data analysis for medical diagnosis: using machine learning and temporal abstraction, AI Communications, 11:191–218, 1999.

·   M. Robnik-Šikonja, I. Kononenko. An adaptation of Relief for attribute estimation in regression, Proc. Int. Conf. on Machine Learning ICML-97 – ISBN 1-55860-486-3, Nashville, July 1997, pp. 296–304.

·   M. Robnik-Šikonja, I. Kononenko. Attribute dependencies, understand­ability and split selection in tree based models, Proc. Int. Conf. on Machine Learning ICML-99 – ISBN 1-55860-612-2, Bled, 27–29 June 1999, pp. 344–353.

·   M. Robnik-Šikonja, I. Kononenko. Comprehensible interpretation of relief's estimates. V: BRODLEY, Carla E. (ur.), DANYLUK, Andrea Pohoreckyj (ur.). Machine learning : proceedings of the eighteenth International Conference (ICML 2001) : Williams College, June 28-July 1, 2001, (The Morgan Kaufmann series in machine learning). San Francisco (Calif.): Morgan Kaufmann Publishers, cop. 2001, str. 433-440.

·   M. Robnik-Šikonja, D. Cukjati, I. Kononenko Comprehensible evaluation of prognostic factors and prediction of wound healing. Artificial Intelligence in Medicine, 29: 25-38, 2003.

·   M. Robnik-Šikonja, I. Kononenko. Theoretical and Empirical Analysis of ReliefF and RReliefF, Machine Learning Journal, 53: 23-69, 2003.

·   M. Robnik-Šikonja. Improving Random Forests.  Proc. of  ECML/PKDD’2004, pp. 359-370, Pisa, Itally, 2004.

·   M. Robnik-Šikonja, I. Kononenko. Reliable feature evaluation in classification and regression. In LIU, John X. (ed.). Control and learning in robotic systems. New York: Nova Science Publishers, 2006

·   Sadikov, I. Kononenko, F. Weibel. Analyzing coronas of fruits and leaves. In: K. Korotkov (ed.). Measuring energy fields : state of the science, (GDV bioelectrography series, vol. 1). Fair Lawn: Backbone, 2004, p.143-154

·   L. Šajn, M. Kukar, I. Kononenko,
M. Milčinski. Computerized segmentation of whole-body bone scintigrams and its use in automated diagnostics. Comput.methods programs biomed. [Print ed.], 2005, vol. 80, no. 1, str. [47]-55, ilustr.

·   Trampuž, I. Kononenko, V. Rus. Experiental and biophysical effects of the Art of Living Programme, Int. Journal of Psychology – Abstracts of 27th Int. Congress of Psychology, Stockholm, 23–28 July 2000 – ISSN 0020-7594, 35(3/4)12