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References [1] G. Barreto and A. Araújo. Time in self-organizing maps: An overview of models. Int. Journ. of Computer Research, 10(2):139–179, 2001. [2] G. de A. Barreto, A. F. R. Araujo, and S. C. Kremer. A taxonomy for spatiotemporal connectionist networks revisited: the unsupervised case. Neural Computation,15(6): 1255 - 1320, 2003. [3] H.-U. Bauer and T. Villmann. Growing a hypercubical output space in a selforganizing feature map. IEEE Transactions on Neural Networks, 8(2):218–226, 1997. [4] C. M. Bishop, M. Svensén, and C. K. I. Williams. GTM: the generative topographic mapping. Neural Computation 10(1):215-235, 1998. [5] C. M. Bishop, G. E. Hinton, and C. K. I. Williams. GTM through time. Proceedings IEE Fifth International Conference on Artificial Neural Networks, Cambridge, U.K., pages 111-116, 1997. [6] Bühlmann, P., and Wyner, A.J. (1999). Variable length Markov chains. Annals of Statistics, 27:480-513. [7] O. A. Carpinteiro. A hierarchical self-organizing map for sequence recognition. Neural Processing Letters, 9(3):209-220, 1999. [8] G. Chappell and J. Taylor. The temporal Kohonen map. Neural Networks, 6:441–445, 1993. [9] I. Farkas and R. Miikkulainen. Modeling the self-organization of directional selectivity in the primary visual cortex. Proceedings of ICANN’99, Edinburgh, Scotland, pp. 251-256, 1999. [10] M. Hagenbuchner, A. C. Tsoi, and A. Sperduti. A supervised self-organising map for structured data. In N. Allinson, H. Yin, L. Allinson, and J. Slack, editors, Advances in Self-Organising Maps, 21–28. Springer, 2001. [11] M. Hagenbuchner, A. Sperduti, and A.C. Tsoi. A Self-Organizing Map for Adaptive Processing of Structured Data. IEEE Transactions on Neural Networks, 14(3):491– 505, 2003. [12] B. Hammer. On the learnability of recursive data. Mathematics of Control Signals and Systems, 12:62–79, 1999. [13] B. Hammer, A. Micheli, and A. Sperduti. A general framework for unsupervised processing of structured data. In M. Verleysen, editor, European Symposium on Artificial Neural Networks’2002, 389–394. D Facto, 2002. [14] B. Hammer, A. Micheli, M. Strickert, A. Sperduti. A general framework for unsupervised processing of structured data. To appear in: Neurocomputing. [15] B. Hammer, A. Micheli, A. Sperduti. A general framework for self-organizing structure processing neural networks. Technical report TR-03-04 of the Università di Pisa, 2003. [16] J. Joutsensalo and A. Miettinen. Self-organizing operator map for nonlinear dimension reduction. Proceedings ICNN’95, 1:111-114, IEEE, 1995. [17] J. Kangas. On the analysis of pattern sequences by self-organizing maps. PhD thesis, Helsinki University of Technology, Espoo, Finland, 1994. 28
[18] S. Kaski, T. Honkela, K. Lagus, and T. Kohonen. WEBSOM – self-organizing maps of document collections. Neurocomputing, 21(1):101-117, 1998. [19] S. Kaski and J. Sinkkonen. A topography-preserving latent variable model with learning metrics. In: N. Allinson, H. Yin, L. Allinson, and J. Slack, editors, Advances in Self-Organizing Maps, pages 224–229, Springer, 2001. [20] T. Kohonen. The ‘neural’ phonetic typewriter. Computer, 21(3):11-22, 1988. [21] T. Kohonen. Self-Organizing Maps. Springer-Verlag, Berlin, 2001. [22] T. Koskela, M. Varsta, J. Heikkonen, and K. Kaski. Recurrent SOM with local linear models in time series prediction. In M.Verleysen, editor, 6th European Symposium on Artificial Neural Networks,pages 167–172, De facto, 1998. [23] T. Koskela, M. Varsta, J. Heikkonen, and K. Kaski. Time series prediction using recurrent SOM with local linear models. International Journal of Knowledge-based Intelligent Engineering Systens 2(1):60-68, 1998. [24] S. C. Kremer. Spatio-temporal connectionist networks: A taxonomy and review. Neural Computation, 13(2):249–306, 2001. [25] J. Laaksonen, M. Koskela, S. Laakso, and E. Oja. PicSOM – content-based image retrieval with self-organizing maps. Pattern Recognition Letters, 21(13-14):1199- 1207, 2000. [26] J. Lampinen and E. Oja. Self-organizing maps for spatial and temporal AR models. M. Pietikäinen and J. Röning (eds.), Proceedings 6 SCIA, 120-127, Helsinki, Finland, 1989. [27] T. Martinetz and K. Schulten. Topology representing networks. Neural Networks, 7(3):507–522, 1994. [28] T. Martinetz, S.G. Berkovich, and K.J. Schulten. ‘Neural-gas’ networks for vector quantization and its application to time-series prediction. IEEE Transactions on Neural Networks, 4(4):558–569, 1993. [29] J. Ontrup and H. Ritter. Text categorization and semantic browsing with selforganizing maps on non-euclidean spaces. In L. D. Raedt and A. Siebes, editors, Proceedings of PKDD-01, 338–349. Springer, 2001. [30] H. Ritter. Self-organizing maps on non-Euclidian spaces. In: E. Oja and S. Kaski, editors, Kohonen Maps, pages 97–110. Elsevier, 1999. [31] H. Ritter, T. Martinetz, and K. Schulten. Neural Computation and Self-Organizing Maps: An Introduction, Addison-Wesley, 1992. [32] Ron, D., Singer, Y., and Tishby, N. (1996). The power of amnesia. Machine Learning, 25:117-150. [33] J. Sinkkonen and S. Kaski. Clustering based on conditional distributions in an auxiliary space. Neural Computation, 14:217–239, 2002. [34] P. Sommervuo. Self-organizing maps for signal and symbokl sequences, PhD thesis, Helsinki University of Technology, 2000. [35] A. Sperduti. Neural networks for adaptive processing of structured data. In Proc. ICANN 2001, 5–12. Springer, 2001. [36] M. Strickert, T. Bojer, and B. Hammer. Generalized relevance LVQ for time series. In Proc. ICANN’2001, 677–638. Springer, 2001. 29
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Page 3 and 4: This framework directly generalizes
Page 5 and 6: place by the update rule △w j =
Page 7 and 8: ecursive partitioning very much lik
Page 9 and 10: However, the dimensionality of the
Page 11 and 12: In the following, we focus on the c
Page 13 and 14: , ! ! , Fig. 1. Hyperbolic
Page 15 and 16: with reverse indexing notation, i.e
Page 17 and 18: The number of specialized neurons f
Page 19 and 20: to Voegtlin’s results, we observe
Page 21 and 22: * 6 2 6 5 : 8 : 2 8 5 - Type P (0)
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Page 25 and 26: follows: a stands for (0, 0) + µ,
Page 27: 6 Conclusions We have presented a s

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