Self-organizing maps with non-cooperative strategies (SOM-NC)

Antonio Neme, Sergio Hernández, Omar Neme, Leticia Hernández

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

The training scheme in self-organizing maps consists of two phases: i) competition, in which all units intend to become the best matching unit (BMU), and ii) cooperation, in which the BMU allows its neighbor units to adapt their weight vector. In order to study the relevance of cooperation, we present a model in which units do not necessarily cooperate with their neighbors, but follow some strategy. The strategy concept is inherited from game theory, and it establishes whether the BMU will allow or not their neighbors to learn the input stimulus. Different strategies are studied, including unconditional cooperation as in the original model, unconditional defection, and several history-based schemes. Each unit is allowed to change its strategy in accordance with some heuristics. We give evidence of the relevance of non-permanent cooperators units in order to achieve good maps, and we show that self-organization is possible when cooperation is not a constraint.

Original languageEnglish
Title of host publicationAdvances in Self-Organizing Maps - 7th International Workshop, WSOM 2009, Proceedings
Pages200-208
Number of pages9
DOIs
StatePublished - 2009
Event7th International Workshop on Self-Organizing Maps, WSOM 2009 - St. Augustine, FL, United States
Duration: 8 Jun 200910 Jun 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5629 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Workshop on Self-Organizing Maps, WSOM 2009
Country/TerritoryUnited States
CitySt. Augustine, FL
Period8/06/0910/06/09

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