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Efficient Adaptation of Structure Metrics in Prototype-Based Classification

  • Bassam Mokbel
  • Benjamin Paassen
  • Barbara Hammer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8681)

Abstract

More complex data formats and dedicated structure metrics have spurred the development of intuitive machine learning techniques which directly deal with dissimilarity data, such as relational learning vector quantization (RLVQ). The adjustment of metric parameters like relevance weights for basic structural elements constitutes a crucial issue therein, and first methods to automatically learn metric parameters from given data were proposed recently. In this contribution, we investigate a robust learning scheme to adapt metric parameters such as the scoring matrix in sequence alignment in conjunction with prototype learning, and we investigate the suitability of efficient approximations thereof.

Keywords

Edit Distance Dissimilarity Measure Learn Vector Quantization Krein Space Tree Edit Distance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Bernard, M., Boyer, L., Habrard, A., Sebban, M.: Learning probabilistic models of tree edit distance. Pattern Recognition 41(8), 2611–2629 (2008)CrossRefzbMATHGoogle Scholar
  2. 2.
    Boyer, L., Esposito, Y., Habrard, A., Oncina, J., Sebban, M.: Sedil: Software for edit distance learning. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part II. LNCS (LNAI), vol. 5212, pp. 672–677. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Gärtner, T., Garriga, G., Meinl, T. (eds.): Proc. of The Workshop on Mining and Learning with Graphs (2006)Google Scholar
  4. 4.
    Gisbrecht, A., Mokbel, B., Hammer, B.: Relational generative topographic mapping. Neurocomputing 74(9), 1359–1371 (2011)CrossRefGoogle Scholar
  5. 5.
    Habrard, A., Iñesta, J.M., Rizo, D., Sebban, M.: Melody recognition with learned edit distances. In: da Vitoria Lobo, N., Kasparis, T., Roli, F., Kwok, J.T., Georgiopoulos, M., Anagnostopoulos, G.C., Loog, M. (eds.) SSPR&SPR 2008. LNCS, vol. 5342, pp. 86–96. Springer, Heidelberg (2008)Google Scholar
  6. 6.
    Hammer, B., Hasenfuss, A.: Topographic mapping of large dissimilarity data sets. Neural Computation 22(9), 2229–2284 (2010)CrossRefzbMATHMathSciNetGoogle Scholar
  7. 7.
    Hammer, B., Hofmann, D., Schleif, F.-M., Zhu, X.: Learning vector quantization for (dis-)similarities. Neurocomputing (2013) (in Press)Google Scholar
  8. 8.
    Hammer, B., Mokbel, B., Schleif, F.-M., Zhu, X.: White box classification of dissimilarity data. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, S.-B. (eds.) HAIS 2012, Part I. LNCS, vol. 7208, pp. 309–321. Springer, Heidelberg (2012)Google Scholar
  9. 9.
    Kästner, M., Nebel, D., Riedel, M., Biehl, M., Villmann, T.: Differentiable kernels in generalized matrix learning vector quantization. In: ICMLA, pp. 132–137 (2012)Google Scholar
  10. 10.
    Kirstein, S., Denecke, A., Hasler, S., Wersing, H., Gross, H.-M., Körner, E.: A vision architecture for unconstrained and incremental learning of multiple categories. Memetic Computing 1(4), 291–304 (2009)CrossRefGoogle Scholar
  11. 11.
    Lundsteen, C., Phillip, J., Granum, E.: Quantitative analysis of 6985 digitized trypsin G-banded human metaphase chromosomes. Clin. Genet. 18, 355–370 (1980)CrossRefGoogle Scholar
  12. 12.
    Mokbel, B., Paassen, B., Hammer, B.: Adaptive distance measures for sequential data. In: Verleysen, M. (ed.) ESANN, pp. 265–270 (2014), i6doc.comGoogle Scholar
  13. 13.
    Pekalska, E., Duin, B.: The Dissimilarity Representation for Pattern Recognition. Foundations and Applications. World Scientific (2005)Google Scholar
  14. 14.
    Schleif, F.-M., Hammer, B., Kostrzewa, M., Villmann, T.: Exploration of mass-spectrometric data in clinical proteomics using learning vector quantization methods. Briefings in Bioinformatics 9(2), 129–143 (2008)Google Scholar
  15. 15.
    Schneider, P., Biehl, M., Hammer, B.: Adaptive relevance matrices in learning vector quantization. Neural Computation 21(12), 3532–3561 (2009)CrossRefzbMATHMathSciNetGoogle Scholar
  16. 16.
    Sperschneider, V.: Bioinformatics. Springer (2008)Google Scholar
  17. 17.
    Takasu, A., Fukagawa, D., Akutsu, T.: Statistical learning algorithm for tree similarity. In: IEEE Int. Conf. on Data Mining, ICDM, pp. 667–672 (2007)Google Scholar
  18. 18.
    van der Maaten, L., Hinton, G.: Visualizing high-dimensional data using t-sne. Journal of Machine Learning Research 9, 2579–2605 (2008)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Bassam Mokbel
    • 1
  • Benjamin Paassen
    • 1
  • Barbara Hammer
    • 1
  1. 1.CITEC centre of excellenceBielefeld UniversityGermany

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