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Lukasz A. Kurgan: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Lukasz A. Kurgan, Krzysztof J. Cios
    Fast Class-Attribute Interdependence Maximization (CAIM) Discretization Algorithm. [Citation Graph (0, 0)][DBLP]
    ICMLA, 2003, pp:30-36 [Conf]
  2. Lukasz A. Kurgan, Waldemar Swiercz, Krzysztof J. Cios
    Semantic Mapping of XML Tags Using Inductive Machine Learning. [Citation Graph (0, 0)][DBLP]
    ICMLA, 2002, pp:99-109 [Conf]
  3. Lukasz A. Kurgan, Leila Homaeian
    Prediction of Secondary Protein Structure Content from Primary Sequence Alone - A Feature Selection Based Approach. [Citation Graph (0, 0)][DBLP]
    MLDM, 2005, pp:334-345 [Conf]
  4. Lukasz A. Kurgan, Krzysztof J. Cios, Ryszard Tadeusiewicz, Marek R. Ogiela, Lucy S. Goodenday
    Knowledge discovery approach to automated cardiac SPECT diagnosis. [Citation Graph (0, 0)][DBLP]
    Artificial Intelligence in Medicine, 2001, v:23, n:2, pp:149-169 [Journal]
  5. Jishou Ruan, Kui Wang, Jie Yang, Lukasz A. Kurgan, Krzysztof J. Cios
    Highly accurate and consistent method for prediction of helix and strand content from primary protein sequences. [Citation Graph (0, 0)][DBLP]
    Artificial Intelligence in Medicine, 2005, v:35, n:1-2, pp:19-35 [Journal]
  6. Kanaka Durga Kedarisetti, Lukasz A. Kurgan, Scott Dick
    A comment on "Prediction of protein structural classes by a new measure of information discrepancy". [Citation Graph (0, 0)][DBLP]
    Computational Biology and Chemistry, 2006, v:30, n:5, pp:393-394 [Journal]
  7. Wojciech Stach, Lukasz A. Kurgan, Witold Pedrycz, Marek Reformat
    Genetic learning of fuzzy cognitive maps. [Citation Graph (0, 0)][DBLP]
    Fuzzy Sets and Systems, 2005, v:153, n:3, pp:371-401 [Journal]
  8. Krzysztof J. Cios, Lukasz A. Kurgan
    CLIP4: Hybrid inductive machine learning algorithm that generates inequality rules. [Citation Graph (0, 0)][DBLP]
    Inf. Sci., 2004, v:163, n:1-3, pp:37-83 [Journal]
  9. Lukasz A. Kurgan, Leila Homaeian
    Prediction of structural classes for protein sequences and domains - Impact of prediction algorithms, sequence representation and homology, and test procedures on accuracy. [Citation Graph (0, 0)][DBLP]
    Pattern Recognition, 2006, v:39, n:12, pp:2323-2343 [Journal]
  10. Lukasz A. Kurgan, Krzysztof J. Cios
    CAIM Discretization Algorithm. [Citation Graph (0, 0)][DBLP]
    IEEE Trans. Knowl. Data Eng., 2004, v:16, n:2, pp:145-153 [Journal]
  11. Lukasz A. Kurgan, Krzysztof J. Cios, Scott Dick
    Highly scalable and robust rule learner: performance evaluation and comparison. [Citation Graph (0, 0)][DBLP]
    IEEE Transactions on Systems, Man, and Cybernetics, Part B, 2006, v:36, n:1, pp:32-53 [Journal]

  12. Prediction of the Number of Helices for the Twilight Zone Proteins. [Citation Graph (, )][DBLP]


  13. Optimization of the Sliding Window Size for Protein Structure Prediction. [Citation Graph (, )][DBLP]


  14. Comparative Analysis of the Impact of Discretization on the Classification with Naïve Bayes and Semi-Naïve Bayes Classifiers. [Citation Graph (, )][DBLP]


  15. Reducing complexity of rule based models via meta mining. [Citation Graph (, )][DBLP]


  16. Multi-label associative classification of medical documents from MEDLINE. [Citation Graph (, )][DBLP]


  17. Impact of the Predicted Protein Structural Content on Prediction of Structural Classes for the Twilight Zone Proteins. [Citation Graph (, )][DBLP]


  18. Parallel Learning of Large Fuzzy Cognitive Maps. [Citation Graph (, )][DBLP]


  19. Classification of Cell Membrane Proteins. [Citation Graph (, )][DBLP]


  20. Use of OWL 2 to Facilitate a Biomedical Knowledge Base Extracted from the GENIA Corpus. [Citation Graph (, )][DBLP]


  21. PFRES: protein fold classification by using evolutionary information and predicted secondary structure. [Citation Graph (, )][DBLP]


  22. Accurate sequence-based prediction of catalytic residues. [Citation Graph (, )][DBLP]


  23. Improved machine learning method for analysis of gas phase chemistry of peptides. [Citation Graph (, )][DBLP]


  24. Sequence based residue depth prediction using evolutionary information and predicted secondary structure. [Citation Graph (, )][DBLP]


  25. Prediction of beta-turns at over 80% accuracy based on an ensemble of predicted secondary structures and multiple alignments. [Citation Graph (, )][DBLP]


  26. SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences. [Citation Graph (, )][DBLP]


  27. Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences. [Citation Graph (, )][DBLP]


  28. A tree-projection-based algorithm for multi-label recurrent-item associative-classification rule generation. [Citation Graph (, )][DBLP]


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