Self-Organizing Kohonen Networks

2016 ◽  
pp. 157-172 ◽  
Author(s):  
Ivan Nunes da Silva ◽  
Danilo Hernane Spatti ◽  
Rogerio Andrade Flauzino ◽  
Luisa Helena Bartocci Liboni ◽  
Silas Franco dos Reis Alves
Keyword(s):  
Kohonen Networks ◽  
Self Organizing

Belemnites of the Neobelemnella kazimiroviensis group – a comparison of the self-organizing Kohonen networks algorithm with the classic palaeopopulation statistical approach and approaching their origin

2020 ◽  
Vol 295 (1) ◽  
pp. 23-51
Author(s):  
Norbert Keutgen ◽  
Anna J. Keutgen
Keyword(s):  
Neural Networks ◽  
The Netherlands ◽  
Statistical Approach ◽  
Hierarchical Cluster ◽  
The Self ◽  
Central Russia ◽  
Group A ◽  
Kohonen Networks ◽  
Upper Maastrichtian ◽  
Self Organizing

Belemnites of the Neobelemnella kazimiroviensis group were classified applying the Artificial Neural Networks method, the self-organizing Kohonen algorithm. Four species are distinguished, Neobelemnella kazimiroviensis (Skołozdrówna, 1932), Neobelemnella pensaensis (Naidin, 1952), Neobelemnella skolozdrownae (Kongiel, 1962), and Neobelemnella aff. kazimiroviensis (Skołozdrówna, 1932). The first two species occur in the Upper Maastrichtian of Central Asia (Kazakhstan, Turkmenistan), Russia, Poland, Denmark, the Netherlands and Belgium. N. skolozdrownae is limited to Poland, Denmark, the Netherlands and Belgium, while N. aff. kazimiroviensis occurs in the Volga Basin (Russia) and Kazakhstan. The evolution of the N. kazimiroviensis group from a member of the Belemnella praearkhangelskii group of Central Russia or Kazakhstan or from the Belemnitella americana group from New Jersey (USA) is discussed applying Hierarchical Cluster Analysis and Multidimensional Comparative Analysis. A member of the Bt. americana group – ? Neo-belemnella subfusiformis (Whitfield, 1892) – is referred to the genus Neobelemnella Naidin, 1975 albeit with a query. This supports the hypothesis that the N. kazimiroviensis group could have evolved from a North American precursor.

scholarly journals Quantitative criteria for recognizing the incorrect behavior of computer network users

2018 ◽  
Vol 11 (3) ◽  
pp. 19-35
Author(s):  
L.S. Kuravsky ◽  
G.A. Yuryev ◽  
P.V. Scribtsov ◽  
M.A. Chervonenkis ◽  
A.A. Konstantinovsky ◽  
...  
Keyword(s):  
Markov Chains ◽  
Computer Network ◽  
Hypotheses Testing ◽  
Feature Maps ◽  
Statistical Hypotheses ◽  
Kohonen Networks ◽  
Statistical Hypotheses Testing ◽  
Dangerous Activity ◽  
Self Organizing

Two approaches for recognizing the incorrect behavior of computer network users are presented. The first one relies on the technique of statistical hypotheses testing and uses self-organizing feature maps (Kohonen networks) for generating target statistics. The second approach recognizes dangerous activity using executed sequences of relevant typical actions, with their dynamics being represented with the aid of Markov chains.

Modeling student knowledge with self-organizing feature maps.

1993 ◽  
Author(s):  
Steven A. Harp ◽  
Tariq Samad ◽  
Michael Villano
Keyword(s):  
Feature Maps ◽  
Student Knowledge ◽  
Self Organizing

Art as a Self-Organizing Complex System

1998 ◽  
Author(s):  
Svetlana Apenova ◽  
Igor Yevin
Keyword(s):  
Complex System ◽  
Self Organizing

Gestalt Self-Organizing Forces in Visual Memory

1992 ◽  
Author(s):  
Lewis O. Harvey ◽  
Anne Igel ◽  
Eric K. Schmidt
Keyword(s):  
Visual Memory ◽  
Self Organizing
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