Featureless Pattern Recognition in an Imaginary Hilbert Space

  title={Featureless Pattern Recognition in an Imaginary Hilbert Space},
  author={Vadim Mottl and Oleg Sergeevich Seredin and Sergey Dvoenko and Casimir A. Kulikowski and Ilya B. Muchnik},
The featureless pattern recognition methodology based on measuring some numerical characteristics of similarity between pairs of entities is applied to the problem of protein fold classification. In computational biology, a commonly adopted way of measuring the likelihood that two proteins have the same evolutionary origin is calculating the so-called alignment score between two amino acid sequences that shows properties of inner product rather than those of a similarity measure. Therefore, in… CONTINUE READING


Publications referenced by this paper.
Showing 1-10 of 12 references

Alignment Scores in a Regularized Support Vector Classification Method for Fold Recognition of Remote Protein Families

  • V. Mottl, S. Dvoenko, O. Seredin, C. Kulikowski, I. Muchnik
  • DIMACS Technical Report 2001-01,
  • 2001
1 Excerpt

Recognition of a protein fold in the context of the SCOP classi cation

  • I. Muchnik, C. Mayor, I. Dralyuk, S.-H. Kim.
  • Statistical Learning Theory
  • 1998

Experiments with a featureless approach to pattern recognition

  • Duin, R.P.W, D. De Ridder, D.M.J. Tax
  • Pattern Recognition Letters,
  • 1997
1 Excerpt

Featureless classification

  • Duin, R.P.W, D. De Ridder, D.M.J. Tax
  • Proceedings of the Workshop on Statistical…
  • 1997
1 Excerpt

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