Please use this identifier to cite or link to this item: http://hdl.handle.net/10525/306

 Title: Almost Separable Data Aggregation by Layers of Formal Neurons Authors: Bobrowski, Leon Keywords: data aggregationayers of formal neurons, separability principles Issue Date: 2008 Publisher: Institute of Information Theories and Applications FOI ITHEA Abstract: Information extraction or knowledge discovery from large data sets should be linked to data aggregation process. Data aggregation process can result in a new data representation with decreased number of objects of a given set. A deterministic approach to separable data aggregation means a lesser number of objects without mixing of objects from different categories. A statistical approach is less restrictive and allows for almost separable data aggregation with a low level of mixing of objects from different categories. Layers of formal neurons can be designed for the purpose of data aggregation both in the case of deterministic and statistical approach. The proposed designing method is based on minimization of the of the convex and piecewise linear (CPL) criterion functions. URI: http://hdl.handle.net/10525/306 ISSN: 1313-0463 Appears in Collections: Volume 15 Number 1

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