Supervised pattern recognition with heterogeneous features

Authors:
Ventzeslav Valev
Affiliations:
Saint Louis University, College of Art and Sciences, Department of Mathematics and Computer Science, St. Louis, MO
Venue:
Machine Graphics & Vision International Journal
Year:
2004

Citing 5
Cited 0

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Fuzzy non-reducible descriptors

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Abstract

In this paper, we address the supervised pattern recognition problem with heterogeneous features, where the mathematical model is based on construction of thresholds. Non-Reducible Descriptors (NRDs) for fuzzy features are obtained through the use of a threshold value, which is calculated based on the distance between patterns. In case of solving the problem with real features, the mathematical model for construction of thresholds is based on parallel feature partitioning. Boolean formulas are used to represent NRDs.