Title :
Formulation of the MACE filter as a linear associative memory
Author :
Fisher, John W., III ; Principe, Jose C.
Author_Institution :
Dept. of Electr. Eng., Florida Univ., Gainesville, FL, USA
fDate :
27 Jun-2 Jul 1994
Abstract :
Shows that the minimum average correlation energy (MACE) filter can be formulated as a linear associative memory (LAM) trained with pre-whitened exemplars. Using this formulation, iterative methods which are numerically stable can be used to obtain the equivalent MACE filter coefficients in the space domain
Keywords :
content-addressable storage; iterative methods; matrix algebra; optimisation; pattern recognition; iterative methods; linear associative memory; minimum average correlation energy filter; pre-whitened exemplars; space domain; Associative memory; Constraint optimization; Equations; Frequency domain analysis; Image recognition; Instruction sets; Iterative algorithms; Least squares approximation; Nonlinear filters; Pattern recognition;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374698
