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Multilayer perceptron neural networks possess pattern recognition properties that make them well suited for use in automatic target recognition systems. Their application is hindered, however, by the lack of a training algorithm, which reliably finds a nearly global optimal set of weights in a relatively short time. The approach presented here is based on(More)
Learning and data clustering with an RBF-based spiking neuron network Natacha Gueorguieva a , Iren Valova b & Georgi Georgiev c a Computer Science , CSI/City University of New York , 2800 Victory Boulevard, Staten Island, NY 10314, USA b Computer Science , University of Massachusetts Dartmouth , 285 Old Westport Road, N. Dartmouth, MA 02747, USA c Computer(More)
With the significant increase of available item listings in popular online auction houses nowadays, it becomes nearly impossible to manually investigate the large amount of auctions and bidders for shill bidding activities, which are a major type of auction fraud in online auctions. Automated mechanisms such as data mining techniques were proven to be(More)
We develop a general algorithm for decomposition and compression of grayscale images. The decomposition can be expressed as a functional relation between the original image and the Hadamard waveforms. The dynamic adaptive clustering procedure incorporates potential functions as a similarity measure for clustering as well as a reclustering phase. The latter(More)
Identifying bidders with suspicious bidding activities related to possible online auction fraud is a difficult task due to a large number of users participating in online auctions. In order to reduce the number of users to be investigated, we examine observable features of a bidder’s behavior, and utilize a hierarchical clustering technique to divide a(More)
Self-Organizing maps (SOM) have become popular for tasks in data visualization, pattern classification or natural language processing and can be seen as one of the major concepts for artificial neural networks of today. Their general idea is to approximate a high dimensional and previously unknown input distribution by a lower dimensional neural network(More)