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Abstract: This paper describes a new method, HGV2C, for pattern analysis. The HGV2Cmethod involves the construction of a computer ego CE based on an individualobject that can be either a part of the system under analysis or a newlycreated object based on a certain hypothesis. The CE provides a capability toanalyze data from a specific standpoint, e.g. from a viewpoint of a certainobject. The CE is constructed from two identical copies of a query object, andits functioning mechanism involves: a hypothesis-parameter HP andinfothyristor IT. HP is a parameter that is introduced into an existing setof parameters. The HP value for one of the clones of a query object is set toequal 1, whereas for another clone it is greater than 1. The IT is based on thepreviously described algorithm of iterative averaging and performs threefunctions: 1 computation of a similarity matrix for the group of three objectsincluding two clones of a query object and a target object; 2 division of thegroup into two alternative subgroups; and 3 a successive increase of the HPweight in the totality of all the parameters. Initially, both clones of thequery object appear together in one of the subgroups as all of their parametervalues, except the HP, are identical. At a certain point of the HPmultiplication, one of the clones moves to the group of the target object. Arespective number of the HP multiplications represents the dissimilarity Dbetween the query and target objects. The product of D multiplied by thedifference in HP values of the clones is strictly constant and linearlyincreases as the difference in HP values of the clones decreases. This newapproach to knowledge representation is demonstrated on the example ofpopulation pyramids of 220 countries.



Autor: Leonid Andreev

Fuente: https://arxiv.org/



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