Classifying Patterns Using Fuzzy Cognitive Maps
This chapter is focused on the use of Fuzzy Cognitive Maps (FCMs) in classifying patterns, as alternative to the traditional classifiers such as neural networks or even as collaborators, in achieving better classification capabilities. By defining the classification procedure as the equilibrium point achieved by applying common inference laws, a FCM can simulate a typical classifier that maps a set of inputs to specific output values.
The classification capabilities of the FCM classifiers are studied in several pattern classification problems, while the ability of the FCM to store knowledge about the problem in hand is investigated in conjunction to the nodes’ type of activation function and the inference law used. Appropriate experiments are taken place, in order to analyze the behavior of the FCM-based classifiers, in well known benchmark problems.
KeywordsZernike Moment Classification Capability Neural Classifier Pattern Recognition Application Good Classification Rate
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