Computational Intelligence -
Learning with Neural
Methods on Structured Data
Papers, Posters, Publications of Marc Strickert
PhD Thesis (preliminary version)
Dipl. Systemwiss Marc Strickert, Self-Organizing Neural Networks for Sequence Processing. Research group 'Learning with Neural Methods on structured data' (LNM), Institute of Computer Science, University of Osnabrück, D-49069 Osnabrück, Germany. Submitted 7 June 2004. Currently under review. PDF: diss_strickert.pdf (approx 1.5MB)
Articles / Papers
B.Hammer, M.Strickert, T.Villmann, Supervised Neural Gas with General Similarity Measure. To appear in Neural Processing Letters. Postscript: ksrng_npl_gz.ps PDF: ksrng_npl.pdf
B.Hammer, M.Strickert, T.Villmann, Prototype based recognition of splice sites. To appear in U.Seiffert et al., Bioinformatics using computational intelligence. Postscript: protosplice_gz.ps PDF: protosplice.pdf
B.Hammer, M.Strickert, T.Villmann, Relevance LVQ versus SVM. In: L.Rutkowski, J.Siekmann, R.Tadeusiewicz, L.A.Zadeh, Artificial Intelligence and Softcomputing, Springer Lecture Notes in Artificial Intelligence 3070, 592-597, 2004. Postscript: lvqvssvm_icaisc04_gz.ps PDF: lvqvssvm_icaisc04.pdf
M. Strickert, B. Hammer, and S. Blohm, Unsupervised Recursive Sequence Processing. Submitted to Elsevier Science, 2004. Postscript: hsomsd_gz.ps PDF: hsomsd.pdf
M. Strickert and B. Hammer, Self-Organizing Context Learning. In: M.Verleysen (ed.), European Symposium on Artificial Neural Networks (ESANN 2004), D-side publications, pp. 39-44, 2004 Postscript: hsoc_strickert_gz.ps PDF: soc_strickert.pdf
T. Bojer, B. Hammer, M. Strickert, and T. Villmann, Determining relevant input dimensions for the self-organizing map. In: L. Rutkowski and J. Kacprzyk (eds.), Neural Networks and Soft Computing (Proc. ICNNSC 2002), Physica-Verlag, 388-393, 2003. Postscript: som_pruning_gz.ps PDF: som_pruning.pdf
Marc Strickert, Barbara Hammer. Neural Gas for Sequences. In T. Yamakawa (ed.), Intelligent Systems and Innovational Computing, Proceedings of the Workshop on Self-Organizing Networks (WSOM 2003), Kyushu Institute of Technology, 53-58, 2003 Postscript: msom_strickert_gz.ps PDF: msom_strickert.pdf
Marc Strickert, Barbara Hammer. Unsupervised Recursive Sequence Processing. In: M.Verleysen (ed.), European Symposium on Artificial Neural Networks (ESANN 2003), D-side publications, pp. 27-32, 2003 Postscript: hsoms_gz.ps PDF: hsoms.pdf
B.Hammer, M.Strickert, T.Villmann, On the generalization ability of GRLVQ networks. Osnabrücker Schriften zur Mathematik, Preprint, no. 249, 10/2003. Postscript: gen_grlvq_tr_gz.ps PDF: gen_grlvq_tr.pdf
Barbara Hammer, Marc Strickert, Thomas Villmann. Learning vector quantization for multimodal data. In: J.R.Dorronsoro (Ed.), Artificial Neural Networks, ICANN 2002, Springer, pp. 370-375, 2002. Postscript: icannsrng02_gz.ps PDF: icannsrng02.pdf
Barbara Hammer, Andreas Rechtien, Marc Strickert, Thomas Villmann. Rule extraction from self-organizing maps. In: J.R.Dorronsoro (Ed.), Artificial Neural Networks, ICANN 2002, Springer, pp. 877-882, 2002. Postscript: icannbb02_gz.ps PDF: icannbb02.pdf
Barbara Hammer, Marc Strickert, Thomas Villmann. Supervised neural gas for learning vector quantization. In: D.Polani, J.Kim, T.Martinetz (eds.), Fifth German Workshop on Artificial Life, IOS Press, 9-18, 2002. Postscript: gi02_gz.ps PDF: gi02.pdf
Barbara Hammer, Andreas Rechtien, Marc Strickert, Thomas Villmann. Vector Quantization with Rule Extraction for Mixed Domain Data. Internal Report. Postscript: srngbb.ps PDF: srngbb.pdf
Marc Strickert, Thorsten Bojer, and Barbara Hammer. Generalized Learning Vector Quantization (GRLVQ) for Time Series. In G. Dorffner, H. Bischof, and K. Hornik (eds.), Artificial Neural Networks, ICANN 2001, pp. 677-683, 2001. Postscript: grlvq_ts_gz.ps PDF: grlvq_ts.pdf
