Iren Valova

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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)
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)
Initialization of self-organizing maps is typically based on random vectors within the given input space. The implicit problem with random initialization is the overlap (entanglement) of connections between neu-rons. In this paper, we present a new method of initiali-zation based on a set of self-similar curves known as Hilbert curves. Hilbert curves can be(More)
In this paper we propose a strategy to shape adaptive radial basis functions through potential functions. DYPOF (DYnamic POtential Functions) neural network (NN) is designed based on radial basis functions (RBF) NN with a two-stage training procedure. Static (fixed number of RBF) and dynamic (ability to add or delete one or more RBF) versions of our(More)