The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Experiments with a featureless approach to pattern recognition
Pattern Recognition Letters - special issue on pattern recognition in practice V
Relational discriminant analysis
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Alignment scores in a regularized support vector classification method for fold recognition of remote protein families
Taxonomy of classifiers based on dissimilarity features
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
The dissimilarity space: Bridging structural and statistical pattern recognition
Pattern Recognition Letters
The dissimilarity representation for structural pattern recognition
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
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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 solving the problem of determining the membership of a protein given by its amino acid sequence (primary structure) in one of preset fold classes (spatial structure), we treat the set of all feasible amino acid sequences as a subset of isolated points in an imaginary space in which the linear operations and inner product are defined in an arbitrary unknown manner, but without any conjecture on the dimension, i.e. as a Hilbert space.