next up previous contents
Next: APPENDIX: KEY TO THE Up: No Title Previous: CONCLUSION

References

Alander et al., 1991
Alander, J. T., Frisk, M., Holmström, L., Hämäläinen, A., and Tuominen, J. (1991) Process error detection using self-organizing feature maps. In Kohonen, T., Mäkisara, K., Simula, O., and Kangas, J., editors, Artificial Neural Networks. Proceedings of ICANN'91, International Conference on Artificial Neural Networks, volume II, pages 1229-1232. North-Holland, Amsterdam.

Amari, 1980
Amari, S. (1980) Topographic organization of nerve fields. Bulletin of Mathematical Biology, 42:339-364.

Anderberg, 1973
Anderberg, M. R. (1973) Cluster Analysis for Applications. Academic Press, New York, NY.

Andrews, 1972
Andrews, D. F. (1972) Plots of high-dimensional data. Biometrics, 28:125-136.

Angéniol et al., 1988
Angéniol, B., de La Croix Vaubois, G., and Le Texier, J.-Y. (1988). Self-organizing feature maps and the traveling salesman problem. Neural Networks, 1:289-293.

Back et al., 1996
Back, B., Sere, K., and Vanharanta, H. (1996) Data mining accounting numbers using self-organizing maps. In Alander, J., Honkela, T., and Jakobsson, M., editors, Proceedings of STeP'96, Finnish Artificial Intelligence Conference, pages 35-47. Finnish Artificial Intelligence Society, Vaasa, Finland.

Bellman, 1961
Bellman, R. E. (1961) Adaptive Control Processes: A Guided Tour. Princeton University Press, New Jersey, NJ.

Bezdek and Pal, 1995
Bezdek, J. C. and Pal, N. R. (1995) An index of topological preservation for feature extraction. Pattern Recognition, 28:381-391.

Bishop et al., 1996a
Bishop, C. M., Svensén, M., and Williams, C. K. I. (1996a). EM optimization of latent-variable models. In Touretzky, D. S., Mozer, M. C., and Hasselmo, M. E., editors, Advances in Neural Information Processing Systems 8, pages 465-471. The MIT Press, Cambridge, MA.

Bishop et al., 1996b
Bishop, C. M., Svensén, M., and Williams, C. K. I. (1996b). GTM: a principled alternative to the self-organizing map. In von der Malsburg, C., von Seelen, W., Vorbrüggen, J. C., and Sendhoff, B., editors, Proceedings of ICANN'96, International Conference on Artificial Neural Networks, Lecture Notes in Computer Science, vol. 1112, pages 165-170. Springer, Berlin.

Biswas et al., 1981
Biswas, G., Jain, A. K., and Dubes, R. C. (1981) Evaluation of projection algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 3:701-708.

Blackmore and Miikkulainen, 1993
Blackmore, J. and Miikkulainen, R. (1993) Incremental grid growing: encoding high-dimensional structure into a two-dimensional feature map. In Proceedings of ICNN'93, IEEE International Conference on Neural Networks, volume I, pages 450-455. IEEE Service Center, Piscataway, NJ.

Blayo and Demartines, 1992
Blayo, F. and Demartines, P. (1992) Algorithme de Kohonen: application à l'analyse de données économiques. Bulletin des Schweizerischen Elektrotechnischen Vereins & des Verbandes Schweizerischer Elektrizitätswerke, 83(5):23-26.

Bruske and Sommer, 1995
Bruske, J. and Sommer, G. (1995) Dynamic cell structure learns perfectly topology preserving map. Neural Computation, 7:845-865.

Budinich, 1996
Budinich, M. (1996). A self-organizing neural network for the traveling salesman problem that is competitive with simulated annealing. Neural Computation, 8:416-424.

Carlson, 1991
Carlson, E. (1991) Self-organizing feature maps for appraisal of land value of shore parcels. In Kohonen, T., Mäkisara, K., Simula, O., and Kangas, J., editors, Artificial Neural Networks. Proceedings of ICANN'91, International Conference on Artificial Neural Networks, volume II, pages 1309-1312, North-Holland, Amsterdam.

Chang and Lee, 1973
Chang, C. L. and Lee, R. C. T. (1973) A heuristic relaxation method for nonlinear mapping in cluster analysis. IEEE Transactions on Systems, Man, and Cybernetics, 3:197-200.

Cheng et al., 1994
Cheng, G., Liu, X., and Wu, J. X. (1994) Interactive knowledge discovery through self-organizing feature maps. In Proceedings of WCNN'94, World Congress on Neural Networks, volume IV, pages 430-434. Lawrence Erlbaum, Hillsdale, NJ.

Chernoff, 1973
Chernoff, H. (1973) The use of faces to represent points in k-dimensional space graphically. Journal of the American Statistical Association, 68:361-368.

Cichocki and Unbehauen, 1993
Cichocki, A. and Unbehauen, R. (1993) Neural Networks for Optimization and Signal Processing. John Wiley, Chichester, England.

Cooley and Lohnes, 1971
Cooley, W. W. and Lohnes, P. R. (1971) Multivariate Data Analysis. Wiley, New York, NY.

de Leeuw and Heiser, 1982
de Leeuw, J. and Heiser, W. (1982) Theory of multidimensional scaling. In Krishnaiah, P. R. and Kanal, L. N., editors, Handbook of Statistics, volume 2, pages 285-316. North-Holland, Amsterdam.

Demartines, 1994
Demartines, P. (1994) Analyse de données par réseaux de neurones auto-organisés (Data analysis through self-organized neural networks). PhD thesis, Institut National Polytechnique de Grenoble, Grenoble, France.

Demartines and Hérault, 1997
Demartines, P. and Hérault, J. (1997). Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets. IEEE Transactions on Neural Networks, 8:148-154.

DeMers and Cottrell, 1993
DeMers, D. and Cottrell, G. (1993) Non-linear dimension reduction. In Hanson, S. J., Cowan, J. D., and Giles, C. L., editors, Advances in Neural Information Processing Systems 5, pages 580-587, Morgan Kaufmann, San Mateo, CA.

Dempster et al., 1977
Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977) Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 39:1-38.

Devijver and Kittler, 1982
Devijver, P. A. and Kittler, J. (1982) Pattern Recognition: A Statistical Approach. Prentice Hall, Englewood Cliffs, NJ.

Didday, 1970
Didday, R. L. (1970) The Simulation and Modeling of Distributed Information Processing in the Frog Visual System. PhD thesis, Stanford University.

Didday, 1976
Didday, R. L. (1976) A model of visuomotor mechanisms in the frog optic tectum. Mathematical Biosciences, 30:169-180.

Dixon, 1979
Dixon, J. K. (1979) Pattern recognition with partly missing data. IEEE Transactions on Systems, Man, and Cybernetics, 9:617-621.

du Toit et al., 1986
du Toit, S. H. C., Steyn, A. G. W., and Stumpf, R. H. (1986) Graphical Exploratory Data Analysis. Springer-Verlag, New York, NY.

Erwin et al., 1992
Erwin, E., Obermayer, K., and Schulten, K. (1992). Self-organizing maps: ordering, convergence properties and energy functions. Biological Cybernetics, 67:47-55.

Fayyad, 1996
Fayyad, U. M. (1996). Data mining and knowledge discovery: making sense out of data. IEEE Expert, October, pages 20-25.

Fayyad et al., 1996a
Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. (1996a) Knowledge discovery and data mining: towards a unifying framework. In Simoudis, E., Han, J., and Fayyad, U., editors, Proceedings of KDD'96, Second International Conference on Knowledge Discovery & Data Mining, pages 82-88. AAAI Press, Menlo Park, CA.

Fayyad et al., 1996b
Fayyad, U. M., Piatetsky-Shapiro, G., and Smyth, P., editors (1996b) Advances in Knowledge Discovery and Data Mining. AAAI Press / MIT Press, Menlo Park, CA.

Fayyad et al., 1996c
Fayyad, U. M., Piatetsky-Shapiro, G., and Smyth, P. (1996c) From data mining to knowledge discovery: an overview. In Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R., editors, Advances in Knowledge Discovery and Data Mining, pages 1-34. AAAI Press / MIT Press, Menlo Park, CA.

Flexer, 1997
Flexer, A. (1997) Limitations of self-organizing maps for vector quantization and multidimensional scaling. To appear in Mozer, M. C., Jordan, M. I., and Petsche, T., editors, Advances in Neural Information Processing Systems 9.

Forsyth, 1989
Forsyth, R., editor (1989) Machine Learning: Principles and Techniques. Chapman and Hall, London.

Friedman, 1987
Friedman, J. H. (1987) Exploratory projection pursuit. Journal of the American Statistical Association, 82:249-266.

Friedman and Tukey, 1974
Friedman, J. H. and Tukey, J. W. (1974) A projection pursuit algorithm for exploratory data analysis. IEEE Transactions on Computers, 23:881-890.

Fritzke, 1991
Fritzke, B. (1991) Let it grow - self-organizing feature maps with problem dependent cell structure. In Kohonen, T., Mäkisara, K., Simula, O., and Kangas, J., editors, Artificial Neural Networks. Proceedings of ICANN'91, International Conference on Artificial Neural Networks, volume I, pages 403-408, North-Holland, Amsterdam.

Fritzke, 1994
Fritzke, B. (1994) Growing cell structures--a self-organizing network for unsupervised and supervised learning. Neural Networks, 7:1441-1460.

Fu, 1974
Fu, K. S. (1974) Syntactic Methods in Pattern Recognition. Academic Press, New York, NY.

Fukunaga, 1972
Fukunaga, K. (1972) Introduction to Statistical Pattern Recognition. Academic Press, New York, NY.

Fyfe and Baddeley, 1995
Fyfe, C. and Baddeley, R. (1995) Non-linear data structure extraction using simple Hebbian networks. Biological Cybernetics, 72:533-541.

Gallant, 1994
Gallant, S. I. (1994) Methods for generating or revising context vectors for a plurality of word stems. U.S. Patent number 5,325,298.

Gallant et al., 1992
Gallant, S. I., Caid, W. R., Carleton, J., Hecht-Nielsen, R., Pu Qing, K., and Sudbeck, D. (1992) HNC's MatchPlus system. ACM SIGIR Forum, 26(2):34-38.

Garavaglia, 1993
Garavaglia, S. (1993) A self-organizing map applied to macro and micro analysis of data with dummy variables. In Proceedings of WCNN'93, World Congress on Neural Networks, pages 362-368. Lawrence Erlbaum and INNS Press, Hillsdale, NJ.

Garrido et al., 1995
Garrido, L., Gaitan, V., Serra-Ricart, M., and Calbert, X. (1995) Use of multilayer feedforward neural nets as a display method for multidimensional distributions. International Journal of Neural Systems, 6:273-282.

Gersho, 1979
Gersho, A. (1979) Asymptotically optimal block quantization. IEEE Transactions on Information Theory, 25:373-380.

Goodhill et al., 1995
Goodhill, G. J., Finch, S., and Sejnowski, T. J. (1995) Quantifying neighborhood preservation in topographic mappings. Technical Report INC-9505, Institute for Neural Computation, La Jolla, CA.

Gray, 1984
Gray, R. M. (1984) Vector quantization. IEEE ASSP Magazine, April, pages 4-29.

Grossberg, 1976
Grossberg, S. (1976) On the development of feature detectors in the visual cortex with applications to learning and reaction-diffusion systems. Biological Cybernetics, 21:145-159.

Hair, Jr. et al., 1984
Hair, Jr., J. F., Anderson, R. E., Tatham, R. L., and Black, W. C. (1984) Multivariate Data Analysis with Readings (4th edition 1995). Prentice-Hall, Englewood Cliffs, NJ.

Hämäläinen, 1994
Hämäläinen, A. (1994) A measure of disorder for the self-organizing map. In Proceedings of ICNN'94, IEEE International Conference on Neural Networks, volume II, pages 659-664. IEEE Service Center, Piscataway, NJ.

Hartigan, 1975
Hartigan, J. (1975) Clustering Algorithms. Wiley, New York, NY.

Hastie and Stuetzle, 1989
Hastie, T. and Stuetzle, W. (1989) Principal curves. Journal of the American Statistical Association, 84:502-516.

Haykin, 1994
Haykin, S. (1994) Neural Networks. A Comprehensive Foundation. Macmillan, New York, NY.

Hecht-Nielsen, 1995
Hecht-Nielsen, R. (1995) Replicator neural networks for universal optimal source coding. Science, 269:1860-1863.

Hoaglin, 1982
Hoaglin, D. C. (1982) Exploratory data analysis. In Kotz, S., Johnson, N. L., and Read, C. B., editors, Encyclopedia of Statistical Sciences, volume 2, pages 579-583. Wiley, New York.

Honkela et al., 1996
Honkela, T., Kaski, S., Lagus, K., and Kohonen, T. (1996) Exploration of full-text databases with self-organizing maps. In Proceedings of ICNN'96, IEEE International Conference on Neural Networks, volume I, pages 56-61. IEEE Service Center, Piscataway, NJ.

Hotelling, 1933
Hotelling, H. (1933) Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24:417-441,498-520.

Iivarinen et al., 1994
Iivarinen, J., Kohonen, T., Kangas, J., and Kaski, S. (1994) Visualizing the clusters on the self-organizing map. In Carlsson, C., Järvi, T., and Reponen, T., editors, Proceedings of the Conference on Artificial Intelligence Research in Finland, pages 122-126. Finnish Artificial Intelligence Society, Helsinki, Finland.

Jain and Dubes, 1988
Jain, A. K. and Dubes, R. C. (1988) Algorithms for Clustering Data. Prentice Hall, Englewood Cliffs, NJ.

Jardine and Sibson, 1971
Jardine, N. and Sibson, R. (1971) Mathematical Taxonomy. Wiley, London.

Kangas, 1994
Kangas, J. (1994) On the analysis of pattern sequences by self-organizing maps. PhD thesis, Helsinki University of Technology, Espoo, Finland.

Kaski and Kohonen, 1994
Kaski, S. and Kohonen, T. (1994) Winner-take-all networks for physiological models of competitive learning. Neural Networks, 7:973-984.

Kaski and Kohonen, 1995
Kaski, S. and Kohonen, T. (1995) Structures of welfare and poverty in the world discovered by the self-organizing map. Technical Report A24, Helsinki University of Technology, Laboratory of Computer and Information Science, Espoo, Finland.

Kasslin et al., 1992
Kasslin, M., Kangas, J., and Simula, O. (1992) Process state monitoring using self-organizing maps. In Aleksander, I. and Taylor, J., editors, Artificial Neural Networks, 2. Proceedings of ICANN'92, International Conference on Artificial Neural Networks, pages 1531-1534, North-Holland, Amsterdam.

Kendall, 1975
Kendall, M. (1975) Multivariate Analysis. Charles Griffin & Company, London.

Kohonen, 1981
Kohonen, T. (1981) Construction of similarity diagrams for phonemes by a self-organizing algorithm. Report TKK-F-A463, Helsinki University of Technology, Espoo, Finland.

Kohonen, 1982
Kohonen, T. (1982) Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43:59-69.

Kohonen, 1984
Kohonen, T. (1984) Self-Organization and Associative Memory. (3rd edition 1989). Springer, Berlin.

Kohonen, 1990
Kohonen, T. (1990) The Self-Organizing Map. Proceedings of the IEEE, 78:1464-1480.

Kohonen, 1991
Kohonen, T. (1991) Self-organizing maps: optimization approaches. In Kohonen, T., Mäkisara, K., Simula, O., and Kangas, J., editors, Artificial Neural Networks. Proceedings of ICANN'91, International Conference on Artificial Neural Networks, volume II, pages 981-990, North-Holland, Amsterdam.

Kohonen, 1993
Kohonen, T. (1993) Physiological interpretation of the self-organizing map algorithm. Neural Networks, 6:895-905.

Kohonen, 1995a
Kohonen, T. (1995a) The adaptive-subspace SOM (ASSOM) and its use for the implementation of invariant feature detection. In Fogelman-Soulié, F. and Gallinari, P., editors, Proceedings of ICANN'95, International Conference on Artificial Neural Networks, volume 1, pages 3-10. EC2 & Cie, Paris.

Kohonen, 1995b
Kohonen, T. (1995b) Emergence of invariant-feature detectors in self-organization. In Palaniswami, M., Attikiouzel, Y., Marks II, R. J., Fogel, D., and Fukuda, T., editors, Computational intelligence. A dynamic system perspective, pages 17-31. IEEE Press, New York, NY.

Kohonen, 1995c
Kohonen, T. (1995c) Self-Organizing Maps. Springer, Berlin.

Kohonen, 1996
Kohonen, T. (1996) Emergence of invariant-feature detectors in the adaptive-subspace self-organizing map. Biological Cybernetics, 75:281-291.

Kohonen et al., 1996a
Kohonen, T., Hynninen, J., Kangas, J., and Laaksonen, J. (1996a) SOM_PAK: the self-organizing map program package. Technical Report A31, Helsinki University of Technology, Laboratory of Computer and Information Science, Espoo, Finland.

Kohonen et al., 1996b
Kohonen, T., Oja, E., Simula, O., Visa, A., and Kangas, J. (1996b). Engineering applications of the self-organizing map. Proceedings of the IEEE, 84:1358-1384.

Koikkalainen, 1994
Koikkalainen, P. (1994) Progress with the tree-structured self-organizing map. In Cohn, A. G., editor, Proceedings of ECAI'94, 11th European Conference on Artificial Intelligence, pages 211-215, Wiley, Chichester, England.

Koikkalainen, 1995
Koikkalainen, P. (1995) Fast deterministic self-organizing maps. In Fogelman-Soulié, F. and Gallinari, P., editors, Proceedings of ICANN'95, International Conference on Artificial Neural Networks, volume II, pages 63-68, EC2 & Cie, Paris.

Koikkalainen and Oja, 1990
Koikkalainen, P. and Oja, E. (1990) Self-organizing hierarchical feature maps. In Proceedings of IJCNN'90 (San Diego), International Joint Conference on Neural Networks, volume II, pages 279-284, IEEE Service Center, Piscataway, NJ.

Kraaijveld et al., 1992
Kraaijveld, M. A., Mao, J., and Jain, A. K. (1992) A non-linear projection method based on Kohonen's topology preserving maps. In Proceedings of 11ICPR, 11th International Conference on Pattern Recognition, pages 41-45, IEEE Computer Society Press, Los Alamitos, CA.

Kraaijveld et al., 1995
Kraaijveld, M. A., Mao, J., and Jain, A. K. (1995) A nonlinear projection method based on Kohonen's topology preserving maps. IEEE Transactions on Neural Networks, 6:548-559.

Kruskal, 1964
Kruskal, J. B. (1964) Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29:1-27.

Kruskal and Wish, 1978
Kruskal, J. B. and Wish, M. (1978) Multidimensional Scaling. Sage University Paper series on Quantitative Applications in the Social Sciences, number 07-011. Sage Publications, Newbury Park, CA.

Lampinen and Oja, 1992
Lampinen, J. and Oja, E. (1992) Clustering properties of hierarchical self-organizing maps. Journal of Mathematical Imaging and Vision, 2:261-272.

Langley, 1996
Langley, P. (1996) Elements of Machine Learning. Morgan Kaufmann, San Francisco, CA.

Lee et al., 1977
Lee, R. C. T., Slagle, J. R., and Blum, H. (1977) A triangulation method for the sequential mapping of points from N-space to two-space. IEEE Transactions on Computers, 26:288-292.

Lloyd, 1957
Lloyd, S. P. (1957). Least squares quantization in PCM. Unpublished memorandum, Bell Laboratories. (Published in IEEE Transactions on Information Theory, 28:129-137, 1982).

Lopes da Silva et al., 1986
Lopes da Silva, F. H., Storm van Leeuwen, W., and Rémond, A., editors (1986) Handbook of Electroencephalography and Clinical Neurophysiology. Volume 2: Clinical Applications of Computer Analysis of EEG and other Neurophysiological Signals. Elsevier, Amsterdam.

Luttrell, 1988
Luttrell, P. S. (1988). Self-organizing multilayer topographic mappings. In Proceedings of ICNN'88, IEEE International Conference on Neural Networks, volume I, pages 93-100. IEEE Service Center, Piscataway, NJ.

Luttrell, 1989
Luttrell, S. P. (1989). Hierarchical vector quantization. IEE Proceedings, 136:405-413.

MacQueen, 1967
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Le Cam, L. M. and Neyman, J., editors, Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. Volume I: Statistics, pages 281-297. University of California Press, Berkeley and Los Angeles, CA.

Makhoul et al., 1985
Makhoul, J., Roucos, S., and Gish, H. (1985) Vector quantization in speech coding. Proceedings of the IEEE, 73:1551-1588.

Mao and Jain, 1995
Mao, J. and Jain, A. K. (1995) Artificial neural networks for feature extraction and multivariate data projection. IEEE Transactions on Neural Networks, 6:296-317.

Martın-del-Brıo and Serrano-Cinca, 1993
Martın-del-Brıo, B. and Serrano-Cinca, C. (1993) Self-organizing neural networks for the analysis and representation of data: some financial cases. Neural Computing & Applications, 1:193-206.

Martinetz and Schulten, 1991
Martinetz, T. and Schulten, K. (1991) A ``neural-gas'' network learns topologies. In Kohonen, T., Mäkisara, K., Simula, O., and Kangas, J., editors, Artificial Neural Networks. Proceedings of ICANN'91, International Conference on Artificial Neural Networks, volume I, pages 397-402. North-Holland, Amsterdam.

Martinetz and Schulten, 1994
Martinetz, T. and Schulten, K. (1994) Topology representing networks. Neural Networks, 7:507-522.

Marttinen, 1993
Marttinen, K. (1993) SOM in statistical analysis: supermarket customer profiling. In Bulsari, A. and Saxén, B., editors, Proceedings of the Symposium on Neural Network Research in Finland, pages 75-80. Finnish Artificial Intelligence Society, Turku, Finland.

Michalski, 1983
Michalski, R. S., Carbonell, J., and Mitchell, T., editors (1983) Machine Learning: An Artificial Intelligence Approach. TIOGA Publishing Company, Palo Alto, CA.

Minamimoto et al., 1995
Minamimoto, K., Ikeda, K., and Nakayama, K. (1995) Topology analysis of data space using self-organizing map. In Proceedings of ICNN'95, IEEE International Conference on Neural Networks, pages 789-794. IEEE Service Center, Piscataway, NJ.

Mulier and Cherkassky, 1995
Mulier, F. and Cherkassky, V. (1995) Self-organization as an iterative kernel smoothing process. Neural Computation, 7:1165-1177.

Murtagh, 1995
Murtagh, F. (1995) Interpreting the Kohonen self-organizing feature map using contiguity-constrained clustering. Pattern Recognition Letters, 16:399-408.

Nass and Cooper, 1975
Nass, M. M. and Cooper, L. N. (1975) A theory for the development of feature detecting cells in visual cortex. Biological Cybernetics, 19:1-18.

Niedermeyer and Lopes da Silva, 1987
Niedermeyer, E. and Lopes da Silva, F., editors (1987) Electroencephalography: Basic Principles, Clinical Applications and Related Fields. Urban & Schwarzenberg, Baltimore, second edition.

Nunez, 1981
Nunez, P. L. (1981) Electric Fields of the Brain. The Neurophysics of EEG. Oxford University Press, New York, NY.

Oja, 1983
Oja, E. (1983) Subspace Methods of Pattern Recognition. Research Studies Press, Letchworth, England.

Oja, 1992
Oja, E. (1992) Principal components, minor components, and linear neural networks. Neural Networks, 5:927-935.

Pedrycz and Card, 1992
Pedrycz, W. and Card, H. C. (1992) Linguistic interpretation of self-organizing maps. In IEEE International Conference on Fuzzy Systems, pages 371-378. IEEE Service Center, Piscataway, NJ.

Pérez et al., 1975
Pérez, R., Glass, L., and Shlaer, R. J. (1975) Development of specificity in cat visual cortex. Journal of Mathematical Biology, 1:275-288.

Ripley, 1996
Ripley, B. D. (1996). Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge, Great Britain.

Ritter, 1991
Ritter, H. (1991) Asymptotic level density for a class of vector quantization processes. IEEE Transactions on Neural Networks, 2:173-175.

Ritter and Kohonen, 1989
Ritter, H. and Kohonen, T. (1989) Self-organizing semantic maps. Biological Cybernetics, 61:241-254.

Ritter et al., 1992
Ritter, H., Martinetz, T., and Schulten, K. (1992) Neural Computation and Self-Organizing Maps: An Introduction. Addison-Wesley, Reading, MA.

Ritter and Schulten, 1988
Ritter, H. and Schulten, K. (1988). Kohonen's self-organizing maps: exploring their computational capabilities. In Proceedings of the ICNN'88, IEEE International Conference on Neural Networks, volume I, pages 109-116. IEEE Service Center, Piscataway, NJ.

Rubner and Tavan, 1989
Rubner, J. and Tavan, P. (1989) A self-organizing network for principal component analysis. Europhysics Letters, 10:693-698.

Rumelhart et al., 1986
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986) Learning internal representations by error propagation. In Rumelhart, D. E., McClelland, J. L., and the PDP Research Group, editors, Paralled Distributed Processing. Explorations in the Microstructure of Cognition. Volume 1: Foundations, pages 318-362. The MIT Press, Cambridge, MA.

Salton and McGill, 1983
Salton, G. and McGill, M. J. (1983) Introduction to Modern Information Retrieval. McGraw-Hill, New York, NY.

Samad and Harp, 1992
Samad, T. and Harp, S. A. (1992) Self-organization with partial data. Network: Computation in Neural Systems, 3:205-212.

Sammon, Jr., 1969
Sammon, Jr., J. W. (1969) A nonlinear mapping for data structure analysis. IEEE Transactions on Computers, 18:401-409.

Schalkoff, 1992
Schalkoff, R. J. (1992) Pattern Recognition: Statistical, Structural and Neural Approaches. Wiley, New York, NY.

Sedgewick, 1988
Sedgewick, R. (1988) Algorithms. Addison-Wesley, Reading, MA, 2nd edition.

Serrano-Cinca, 1996
Serrano-Cinca, C. (1996) Self-organizing neural networks for financial diagnosis. To appear in Decision Support Systems.

Shepard, 1962
Shepard, R. N. (1962) The analysis of proximities: multidimensional scaling with an unknown distance function. Psychometrika, 27:125-140; 219-246.

Simoudis, 1996
Simoudis, E. (1996). Reality check for data mining. IEEE Expert, October, pages 26-33.

Sneath and Sokal, 1973
Sneath, P. H. A. and Sokal, R. R. (1973) Numerical Taxonomy. Freeman, San Francisco, CA.

Swindale, 1980
Swindale, N. W. (1980) A model for the formation of ocular dominance stripes. Proceedings of the Royal Society of London, B, 208:243-264.

Therrien, 1989
Therrien, C. W. (1989) Decision, Estimation, and Classification. An Introduction to Pattern Recognition and Related Topics. Wiley, New York, NY.

Torgerson, 1952
Torgerson, W. S. (1952) Multidimensional scaling: I. Theory and method. Psychometrika, 17:401-419.

Tryba and Goser, 1991
Tryba, V. and Goser, K. (1991) Self-organizing feature maps for process control in chemistry. In Artificial Neural Networks. Proceedings of ICANN'91, International Conference on Artificial Neural Networks, volume I, pages 847-852, North-Holland, Amsterdam.

Tryon and Bailey, 1973
Tryon, R. C. and Bailey, D. E. (1973) Cluster Analysis. McGraw-Hill, New York, NY.

Tukey, 1977
Tukey, J. W. (1977) Exploratory Data Analysis. Addison-Wesley, Reading, MA.

Ultsch, 1993a
Ultsch, A. (1993a) Knowledge extraction from self-organizing neural networks. In Opitz, O., Lausen, B., and Klar, R., editors, Information and Classification, pages 301-306. Springer-Verlag, Berlin.

Ultsch, 1993b
Ultsch, A. (1993b) Self-organizing neural networks for visualization and classification. In Opitz, O., Lausen, B., and Klar, R., editors, Information and Classification, pages 307-313. Springer-Verlag, Berlin.

Ultsch and Siemon, 1990
Ultsch, A. and Siemon, H. P. (1990) Kohonen's self organizing feature maps for exploratory data analysis. In Proceedings of ICNN'90, International Neural Network Conference, pages 305-308, Kluwer, Dordrecht.

Varfis and Versino, 1992
Varfis, A. and Versino, C. (1992) Clustering of socio-economic data with Kohonen maps. Neural Network World, 2:813-834.

Velleman and Hoaglin, 1981
Velleman, P. F. and Hoaglin, D. C. (1981) Applications, Basics, and Computing of Exploratory Data Analysis. Duxbury Press, Boston, MA.

von der Malsburg, 1973
von der Malsburg, C. (1973) Self-organization of orientation sensitive cells in the striate cortex. Kybernetik, 14:85-100.

Webb, 1995
Webb, A. R. (1995). Multidimensional scaling by iterative majorization using radial basis functions. Pattern Recognition, 28:753-759.

Wish and Carroll, 1982
Wish, M. and Carroll, J. D. (1982) Multidimensional scaling and its applications. In Krishnaiah, P. R. and Kanal, L. N., editors, Handbook of Statistics, volume 2, pages 317-345. North-Holland, Amsterdam.

World Bank, 1992
World Bank (1992) World Development Report 1992. Oxford University Press, New York, NY.

Young, 1985
Young, F. W. (1985) Multidimensional scaling. In Kotz, S., Johnson, N. L., and Read, C. B., editors, Encyclopedia of Statistical Sciences, volume 5, pages 649-659. Wiley, New York, NY.

Young and Householder, 1938
Young, G. and Householder, A. S. (1938). Discussion of a set of points in terms of their mutual distances. Psychometrika, 3:19-22.

Zador, 1982
Zador, P. L. (1982). Asymptotic quantization error of continuous signals and the quantization dimension. IEEE Transactions on Information Theory, 28:139-149.

Zhang and Li, 1993
Zhang, X. and Li, Y. (1993) Self-organizing map as a new method for clustering and data analysis. In Proceedings of IJCNN'93 (Nagoya), International Joint Conference on Neural Networks, pages 2448-2451. IEEE Service Center, Piscataway, NJ.

Zrehen, 1993
Zrehen, S. (1993) Analyzing Kohonen maps with geometry. In Gielen, S. and Kappen, B., editors, Proceedings of ICANN'93, International Conference on Artificial Neural Networks, pages 609-612, Springer, London.



Sami Kaski
Mon Mar 31 23:43:35 EET DST 1997