Abstract
Membership functions are of vital importance to applications of fuzzy theory. In this paper, we consider several methods for generating membership functions for pattern recognition and computer vision applications. The methods we discuss include heuristic methods, methods based on probability-possibility transformations, methods using feed-forward neural networks, the Fuzzy C Means algorithm, and the Possibilistic C Means algorithm. A qualitative comparison is made.
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© 1995 Kluwer Academic Publishers
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Medasani, S., Kim, J., Krishnapuram, R. (1995). Estimation of Membership Functions for Pattern Recognition and Computer Vision. In: Bien, Z., Min, K.C. (eds) Fuzzy Logic and its Applications to Engineering, Information Sciences, and Intelligent Systems. Theory and Decision Library, vol 16. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-0125-4_5
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DOI: https://doi.org/10.1007/978-94-009-0125-4_5
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-6543-6
Online ISBN: 978-94-009-0125-4
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