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Updated: Aug 18, 2026

09:44
Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
Published on: October 16, 2018
Maximum likelihood topographic map formation
1K.U.Leuven, Laboratorium voor Neuro- en Psychofysiologie, B-3000 Leuven, Belgium. marc@neuro.kuleuven.ac.be
Neural Computation
|April 2, 2005
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.
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