Transformations of symbolic data for continuous data oriented models

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2714, Issue , 2003, Pages 359-366

  Gra̧bczewski, Krzysztof ^a   Jankowski, Norbert ^a  

^a NICOLAUS COPERNICUS UNIVERSITY   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE;

CONTINUOUS ATTRIBUTE; CONTINUOUS DATA; CONVERSION METHODS; DISTANCE-BASED METHODS; SYMBOLIC DATA; SYMBOLIC VALUE;

NEURAL NETWORKS;

EID: 35248853732     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44989-2_43     Document Type: Article

References (13)

1. R. Adamczak, W. Duch, and N. Jankowski. New developments in the feature space mapping model. In Third Conference on Neural Networks and Their Applications, pages 65-70, Kule, Poland, October 1997.
2. E. Blanzieri and F. Ricci. Advanced metrics for class-driven similarity search. In Proceedings of the International Workshop on Similarity Search, Firenze, Italy, September 1999.
3. L. Bobrowski, M. Krȩtowska, and M. Krȩtowski. Design of neural classifying networks by using dipolar criterions. In Third Conference on Neural Networks and Their Applications, Kule, Poland, October 1997.
4. S. Brandt. Data Analysis. Springer, New York, 1999.
5. T. G. Dietterich. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation, 10(7):1895-1924, 1998.
6. W. Duch and G. H. F. Diercksen. Feature space mapping as a universal adaptive system. Computer Physics Communications, 87:341-371, 1995.
7. W. Duch, K. Grudziński, and G. Stawski. Symbolic features in neural networks. In Proceedings of the 5th Conference on Neural Networks and Their Applications, pages 180-185, Zakopane, Poland, June 2000.
8. K. Gra̧bczewski and W. Duch. A general purpose separability criterion for classification systems. In Proceedings of the 4th Conference on Neural Networks and Their Applications, pages 203-208, Zakopane, Poland, June 1999.
9. K. Gra̧bczewski and W. Duch. The separability of split value criterion. In Proceedings of the 5th Conference on Neural Networks and Their Applications, pages 201-208, Zakopane, Poland, June 2000.
10. S. S. Keerthi, S. K. Shevade, C. Bhattacharyya, and K. R. K. Murthy. Improvements to Plait's SMO algorithm for SVM classifier design. Neural Computation, 13:637-649, 2001.
11. C. J. Merz and P. M. Murphy. UCI repository of machine learning databases, 1998. http://www.ics.uci.edu/~mlearn/MLRepository.html.
12. J. C. Platt. Fast training of support vector machines using sequential minimal optimization. In B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods - Support Vector Learning. MIT Press, Cambridge, MA., 1998.
13. D. R. Wilson and T. R. Martinez. Improved heterogeneous distance functions. Journal of Artificial Intelligence Research, 11:1-34, 1997.