Andrea Baraldi

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Clustering algorithms aim at modeling fuzzy (i.e., ambiguous) unlabeled patterns efficiently. Our goal is to propose a theoretical framework where the expressive power of clustering systems can be compared on the basis of a meaningful set of common functional features. Part I of this paper reviews the following issues related to clustering approaches found(More)
Part I of this paper defines the class of constructive unsupervised on-line learning simplified adaptive resonance theory (SART) clustering networks. Proposed instances of class SART are the symmetric Fuzzy ART (S-Fuzzy ART) and the Gaussian ART (GART) network. In Part II of our work, a third network belonging to class SART, termed fully self-organizing(More)
The Simpliied Adaptive Resonance Theory (SART) class of networks is proposed to handle problems encountered in Adaptive Resonance Theory 1 (ART 1)-based algorithms when detection of binary and analog patterns is performed. The basic idea of SART is to substitute ART 1-based \unidirectional" (asymmetric) activation and match functions with \bidirectional"(More)
This article presents an implementation of an artificial neural network (ANN) which performs unsupervised detection of recognition categories from arbitrary sequences of multivalued input patterns. The proposed ANN is called Simplified Adaptive Resonance Theory Neural Network (SARTNN). First, an Improved Adaptive Resonance Theory 1 (IARTl)-based neural(More)
The increasing amount of remote sensing (RS) imagery acquired from multiple platforms and the recent announcements that scientists and decision makers around the world will soon have unrestricted access at no charge to large-scale spaceborne multispectral (MS) image databases make urgent the need to develop easy-to-use, effective, efficient, robust, and(More)