Title: Topographic map formation by maximizing unconditional entropy: A plausible strategy for ''on-line'' unsupervised competitive learning and nonparametric density estimation
Authors: Van Hulle, Marc # ×
Issue Date: 1996
Series Title: IEEE transactions on neural networks vol:7 issue:5 pages:1299-1305
Abstract: An unsupervised competitive learning rule, called the vectorial boundary adaptation rule (VBAR), is introduced for topographic map formation, Since VBAR is aimed at producing an equiprobable quantization of the input space, it yields a nonparametric model of the input probability density function, Furthermore, since equiprobable quantization is equivalent to unconditional entropy maximization, we will argue that this is a plausible strategy for maximizing mutual information (Shannon information rate) in the case of ''on-line'' learning, We will use mutual information as a tool for comparing the performance of our rule with Kohonen's self-organizing (feature) map algorithm.
ISSN: 1045-9227
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Research Group Neurophysiology
× corresponding author
# (joint) last author

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