Symbolic Classifier with Convex Hull Based Dissimilarity Function
This work presents a new symbolic classifier based on a region oriented approach. At the end of the learning step, each class is described by a region (or a set of regions) in ℜp defined by the convex hull of the objects belonging to this class. In the allocation step, the assignment of a new object to a class is based on a dissimilarity matching function that compares the class description (a region or a set of regions) with a point in ℜp. This approach aims to reduce the over-generalization that is produced when each class is described by a region (or a set of regions) defined by the hyper-cube formed by the objects belonging to this class. It then seeks to improve the classifier performance. In order to show its usefulness, this approach was applied to a study of simulated SAR images.
KeywordsSynthetic Aperture Radar Classifier Performance Maximal Clique Class Description Speckle Noise
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