A game-theoretic analysis on adaptive categorization in ART networks

Analysis of a game-theoretic formulation of adaptive categorization in ART-type networks is presented. Classical ART-types networks, however, have only fixed single size clusters formation in categorization, which is controlled by the scalar vigilance parameter ρ. This categorization methodology usually cannot give satisfactory results as the data pattern space is not covered thoroughly by fixed boundary clusters. Analysis on the adapted ρ based on the unique Nash equilibrium of the adaptive categorization game Γ_AC is investigated for parameter selection. ρ-adaptation also helps to solve the difficult problem of choosing a suitable vigilance parameter for data categorization. Simulations of the ρ adaptation rule on patterns from mixture of distributions are presented