title: Maximum likelihood topographic map formation.
abstract: We introduce a new unsupervised learning algorithm for kernel-based topographic map formation of heteroscedastic gaussian mixtures that allows for a unified account of distortion error (vector quantization), log-likelihood, and Kullback-Leibler divergence.
authors.affiliation: K.U.Leuven, Laboratorium voor Neuro- en Psychofysiologie, B-3000 Leuven, Belgium. marc@neuro.kuleuven.ac.be
authors.forename: Marc M
authors.initials: MM
authors.lastname: Van Hulle
mesh_terms: D000465:Algorithms; D001931:Brain Mapping; D003198:Computer Simulation; D006801:Humans; D007858:Learning / Q000502:physiology*; D016013:Likelihood Functions