Fuzzy classification using information theoretic learning vector quantization

Neurocomputing

Volumn 71, Issue 16-18, 2008, Pages 3070-3076

  Villmann, Thomas ^a   Hammer, Barbara ^b   Schleif, Frank Michael ^a   Hermann, Wieland ^c   Cottrell, Marie ^d  

^a UNIVERSITY OF LEIPZIG   (Germany)

^b CLAUSTHAL UNIVERSITY OF TECHNOLOGY   (Germany)

^c Paracelsus Klinik Zwickau   (Germany)

^d University Paris I Sorbonne Panthéon   (France)

Author keywords

Classification; Information theory; Learning vector quantization

Indexed keywords

EDUCATION; FUZZY SYSTEMS; INFORMATION THEORY; LEARNING ALGORITHMS; MEDICAL APPLICATIONS; NEURAL NETWORKS; SUPERVISED LEARNING; VECTORS;

CLASSIFICATION; EUCLIDEAN; FUZZY CLASSIFICATIONS; LEARNING VECTOR QUANTIZATION; LEARNING VECTOR QUANTIZATIONS; METRIC ADAPTATIONS; NEW APPROACHES; ORIGINAL ALGORITHMS; RELEVANCE LEARNINGS; SCHWARZ; SUPERVISED LEARNING METHODS; UNSUPERVISED METHODS; VECTOR QUANTIZATION APPROACHES;

VECTOR QUANTIZATION;

AUTOMATED PATTERN RECOGNITION; CLASSIFICATION ALGORITHM; CONFERENCE PAPER; FUZZY SYSTEM; INFORMATION PROCESSING; LEARNING ALGORITHM; MATHEMATICAL ANALYSIS; MATHEMATICAL COMPUTING; PRIORITY JOURNAL;

EID: 56549083671     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2008.04.048     Document Type: Conference Paper

References (46)

1. S.C. Ahalt, A.K. Krishnamurty, P. Chen, and D.E. Melton Competitive learning algorithms for vector quantization Neural Networks 3 3 1990 277 290
2. C.L. Blake, C.J. Merz, UCI repository of machine learning databases, University of California, Department of Information and Computer Science, Irvine, CA. Available at: 〈http://www.ics.uci.edu/~mlearn/MLRepository. html〉, 1998.
3. R. Brause Neuronale Netze 2nd ed. 1995 B.G. Teubner Stuttgart
4. I. Csiszár Information-type measures of differences of probability distributions and indirect observations Studia Sci. Math. Hungaria 2 1967 299 318
5. G. Deco, and D. Obradovic An Information-Theoretic Approach to Neural Computing 1997 Springer Heidelberg, New York, Berlin
6. D. DeSieno, Adding a conscience to competitive learning, in: Proceedings of the ICNN'88, International Conference on Neural Networks, IEEE Service Center, Piscataway, NJ, 1988, pp. 117-124.
7. D. Erdogmus, Information theoretic learning: Renyi's entropy and its application to adaptive systems training, Ph.D. Thesis, University of Florida, 2002.
8. B. Hammer, M. Strickert, and Th. Villmann Supervised neural gas with general similarity measure Neural Process. Lett. 21 1 2005 21 44
9. B. Hammer, and Th. Villmann Generalized relevance learning vector quantization Neural Networks 15 8-9 2002 1059 1068
10. S. Haykin Neural Networks - A Comprehensive Foundation 1994 IEEE Press New York
11. W. Hermann, H. Barthel, S. Hesse, F. Grahmann, H.-J. Kühn, A. Wagner, and Th. Villmann Comparison of clinical types of Wilson's disease and glucose metabolism in extrapyramidal motor brain regions J. Neurol. 249 7 2002 896 901
12. W. Hermann, B. Eggers, H. Barthel, D. Clark, Th. Villmann, S. Hesse, F. Grahmann, H.-J. Kühn, O. Sabri, and A. Wagner Correlation between automated writing movements and striatal dopaminergic innervation in patients with Wilson's disease J. Neurol. 249 8 2002 1082 1087
13. W. Hermann, P. Günther, H.J. Kühn, T. Villmann, A. Bachhuber, and A. Wagner Pyramidale Schädigung im Vergleich zur extrapyramidalmotorischen Beeinträchtigung bei Patienten mit Morbus Wilson Klin. Neurophysiol. 38 4 2007 216 221
14. W. Hermann, P. Günther, H.J. Kühn, T. Villmann, and A. Wagner Computergestützte Analyse der Handschrift bei Patienten mit Morbus Wilson Klin. Neurophysiol. 38 2007 185 189
15. W. Hermann, A. Wagner, H.-J. Kühn, F. Grahmann, and T. Villmann Classification of fine-motoric disturbances in Wilson's disease using artificial neural networks Acta Neurol. Scand. 111 6 2005 400 406
16. A.K. Jain, R.P.W. Duin, and J. Mao Statistical pattern recognition: a review IEEE Trans. Pattern Anal. Mach. Intell. 22 2000 4 37
17. R. Jenssen, An information theoretic approach to machine learning, Ph.D. Thesis, Department of Physics, University of Tromsø, 2005.
18. J.N. Kapur Measures of Information and their Application 1994 Wiley New Delhi
19. T. Kohonen, Self-Organizing Maps, Springer Series in Information Sciences, vol. 30, Springer, Berlin, Heidelberg, 1995, Second Extended Edition 1997.
20. T. Kostiainen, J. Lampinen, Self-organizing map as a probability density model, in: Proceedings of the International Joint Conference on Neural Networks, Laboratory of Computational Engineering, Helsinki University of Technology, vol. 1, 2001, pp. 394-399.
21. S. Kullback, and R.A. Leibler On information and sufficiency Ann. Math. Stat. 22 1951 79 86
22. P. Kumar, and S. Chhina A symmetric information divergence measure of the Csiszár's f-divergence class and its bounds Comput. Math. Appl. 49 2005 575 588
23. J. Lampinen, and T. Kostiainen Generative probability density model in the self-organizing map U. Seiffert L.C. Jain Self-Organizing Neural Networks, Studies in Fuzziness and Soft Computing 2002 Physica-Verlag Heidelberg, New York 75 92
24. T. Lehn-Schiøler, A. Hegde, D. Erdogmus, and J.C. Principe Vector quantization using information theoretic concepts Nat. Comput. 4 1 2005 39 51
25. J. Lin Divergence measure based on Shannon entropy IEEE Trans. Inf. Theory 37 1 1991 145 151
26. R. Linsker How to generate maps by maximizing the mutual information between input and output signals Neural Comput. 1 1989 402 411
27. E. Oja, and J. Lampinen Unsupervised learning for feature extraction J.M. Zurada R.J. Marks II C.J. Robinson Computational Intelligence Imitating Life 1994 IEEE Press New York 13 22
28. J.C. Principe, J.W. Fischer III, and D. Xu Information theoretic learning S. Haykin Unsupervised Adaptive Filtering 2000 Wiley New York, NY
29. A. Renyi, On measures of entropy and information, in: Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, University of California Press, Berkeley, CA, 1961.
30. A. Renyi Probability Theory 1970 North-Holland Amsterdam
31. A. Sato, K. Yamada, Generalized learning vector quantization, in: D.S. Touretzky, M.C. Mozer, M.E. Hasselmo (Eds.), Advances in Neural Information Processing Systems 8, Proceedings of the 1995 Conference, MIT Press, Cambridge, MA, USA, 1996, pp. 423-429.
32. S. Seo, M. Bode, and K. Obermayer Soft nearest prototype classification IEEE Trans. Neural Networks 14 2003 390 398
33. S. Seo, and K. Obermayer Soft learning vector quantization Neural Comput. 15 2003 1589 1604
34. C.E. Shannon A mathematical theory of communication Bell Syst. Tech. J. 27 1948 379 432
35. B.W. Silverman Density Estimation for Statistics and Data Analysis 1986 Chapman & Hall London
36. I. Taneja, and P. Kumar Relative information of type s, Csiszár's f-divergence, and information inequalities Inf. Sci. 166 2004 105 125
37. K. Torkkola Feature extraction by non-parametric mutual information maximization J. Mach. Learn. Res. 3 2003 1415 1438
38. K. Torkkola, W.M. Campbell, Mutual information in learning feature transformations, in: Proceedings of the International Conference on Machine Learning ICML'2000, Stanford, CA, 2000.
39. M.M. Van Hulle Faithful Representations and Topographic Maps, Wiley Series and Adaptive Learning Systems for Signal Processing, Communications, and Control 2000 Wiley New York
40. M.M. Van Hulle Joint entropy maximization in kernel-based topographic maps Neural Comput. 14 8 2002 1887 1906
41. M. Verleysen, D. François, The curse of dimensionality in data mining and time series prediction, in: J. Cabestany, A. Prieto, F.S. Hernández (Eds.), Computational Intelligence and Bioinspired Systems, Proceedings of the Eighth International Work-Conference on Artificial Neural Networks 2005 (IWANN), Barcelona, Springer, Berlin, 2005, pp. 758-770.
42. T. Villmann, and J.-C. Claussen Magnification control in self-organizing maps and neural gas Neural Comput. 18 2 2006 446 469
43. T. Villmann, F.-M. Schleif, and B. Hammer Prototype-based fuzzy classification with local relevance for proteomics Neurocomputing 69 16-18 2006 2425 2428
44. T. Villmann, F.-M. Schleif, and B. Hammer Comparison of relevance learning vector quantization with other metric adaptive classification methods Neural Networks 19 2006 610 622
45. P.L. Zador Asymptotic quantization error of continuous signals and the quantization dimension IEEE Trans. Inf. Theory 28 1982 149 159
46. K. Życzkowski Rényi extrapolation of shannon entropy Open Syst. Inf. Dyn. 10 2003 297 310