Supervised pattern recognition with heterogeneous features

• Autorzy: Valev V.
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

supervised pattern recognition; heterogeneous features; non-reducible descriptors

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2004
• Tom: Vol. 13, No. 4
• Strony: 345--353
• Opis fizyczny: Bibliogr. 8 poz.

Twórcy

autor

Valev V.

• Saint Louis University, College of Art and Sciences, Department of Mathematics and Computer Science, St. Louis, MO 63103, USA,

Bibliografia

• [1] Rendell L.: A new basis for state-space learning systems and successful implementation. Artificial Intelligence, 20, 369-392, 1983.
• [2] Quinlan J. R.: Induction of decision trees. Machine Learning, 1, 81-106, 1986.
• [3] Aha D. W., Kibler D., Albert M. K.: Instance-based learning algorithms. Machine Learning, 6, 77-66, 1991.
• [4] Salzberg S.: A nearest hyperrectangle method. Machine Learning, 6, 251-276, 1991.
• [5] Valev V., Radeva P.: A Method of solving pattern or image recognition problem by learning Boolean formulas. Proc. of 11th Int. Conf. on Pattern Recognition, Hague, Netherlands, August 30 - September 3, IEEE Computer Society Press, II, 359-362, 1992.
• [6] Güvenir H. A., Sirin I.: Classification by feature partitioning. Machine Learning, 23, 47-67, 1996.
• [7] Valev V., Asaithambi A.: Fuzzy non-reducible descriptors. MG&V, 12(3), 353-361, 2003.
• [8] Valev V.: Supervised pattern recognition by parallel feature partitioning. Pattern Recognition, 37(3), 463-467, 2004.