Abstract
Growing neural gas (GNG) has been successfully applied to unsupervised learning problems. However, GNG-inspired approaches can also be applied to classification problems, provided they are extended with an appropriate labelling function. Most approaches along these lines have so far relied on strategies which label neurons a posteriori, after the training has been completed. As a consequence, such approaches require the training data to be stored until the labelling phase, which runs directly counter to the online nature of GNG. Thus, in order to restore the online property of classification approaches based on GNG, we present an approach in which the labelling is performed online. This online labelling strategy better matches the online nature of GNG where only neurons – but no explicit training examples – are stored. As the main contribution, we show that online labelling strategies do not deteriorate the performance compared to offline labelling strategies.
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References
Anderberg, M.R.: Cluster analysis for applications. Academic Press, New York (1973)
Fritzke, B.: A growing neural gas network learns topologies. In: Proceedings of the International Conference on Advances in Neural Information Processing Systems (NIPS) 1995, pp. 625–632 (1995)
Hyotyniemi, H.: Text document classification with self-organizing maps. In: Proc. of the Finnish Artificial Intelligence Conf. (STeP) 1996, pp. 64–72 (1996)
Johnson, S.C.: Hierarchical clustering schemes. Psychometrika 32(3), 241–254 (1967)
Kohonen, T.: Self-organized formation of topologically correct feature maps. Biological Cybernetics 43(1), 59–69 (1982)
Kohonen, T.: Learning vector quantization. The Handbook of Brain Theory and Neural Networks, pp. 537–540. MIT Press, Cambridge (1995)
Kohonen, T., Kaski, S., Lagus, K.: Self organization of a massive document collection. IEEE Transactions on Neural Networks 11(3), 574–585 (2000)
Lagus, K., Kaski, S.: Keyword selection method for characterizing text document maps. In: Proc. of the Int. Conf. on Artificial Neural Networks (ICANN), pp. 371–376 (1999)
Lau, K., Yin, H., Hubbard, S.: Kernel self-organising maps for classification. Neurocomputing 69(16-18), 2033–2040 (2006)
Lefebvre, G., Garcia, C.: A probabilistic self-organizing map for facial recognition. In: Proc. of the Int. Conf. on Pattern Recognition (ICPR) 2008, pp. 1–4 (2008)
Martinetz, T., Schluten, K.: A ”neural-gas” network learns topologies. In: In Proc. of the Int. Conf. on Artificial Neural Networks (ICANN) 1991, pp. 397–402 (1991)
Rauber, A.: LabelSOM: On the labeling of self-organizing maps. In: Proc. of the Int. Joint Conf. on Neural Networks (IJCNN) 1999, pp. 3527–3532 (1999)
Sneath, P.H.A., Sokal, R.R.: Numerical taxonomy: The principles and practice of numerical classification, 1st edn. W H Freeman & Co. (Sd), New York (1973)
Steil, J.J., Wersing, H.: Recent trends in online learning for cognitive robotics. In: Proc. of the European Symposium on Artificial Neural Networks (ESANN) 2006, pp. 77–87 (2006)
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Beyer, O., Cimiano, P. (2011). Online Labelling Strategies for Growing Neural Gas. In: Yin, H., Wang, W., Rayward-Smith, V. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2011. IDEAL 2011. Lecture Notes in Computer Science, vol 6936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23878-9_10
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DOI: https://doi.org/10.1007/978-3-642-23878-9_10
Publisher Name: Springer, Berlin, Heidelberg
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