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Title: GENERALIZED BOUNDARY ADAPTATION RULE FOR MINIMIZING RTH POWER-LAW DISTORTION IN HIGH-RESOLUTION QUANTIZATION
Authors: MARTINEZ, D ×
Van Hulle, Marc #
Issue Date: 1995
Publisher: PERGAMON-ELSEVIER SCIENCE LTD
Series Title: Neural networks vol:8 issue:6 pages:891-900
Conference: date:KATHOLIEKE UNIV LEUVEN,NEURO & PSYCHOFYSIOL LAB,LOUVAIN,BELGIUM
Abstract: A new generalized unsupervised competitive learning rule is introduced for adaptive scalar quantization. The rule, called the generalized boundary adaptation rule (BAR(r)), minimizes the rth power law distortion D-r in the high resolution case. It ir shown by simulations that a fast version of BAR(r) outperforms generalized Lloyd I in minimizing D-1 (mean absolute error) and D-2 (mean squared error) distortion with substantially less iterations. In addition. since BAR(r) does not require generalized centroid estimation, as in Lloyd I, it is much simpler to implement.
ISSN: 0893-6080
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Research Group Neurophysiology
× corresponding author
# (joint) last author

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