TY - GEN
T1 - Thresholded learning matrix for efficient pattern recalling
AU - Aldape-Pérez, Mario
AU - Román-Godínez, Israel
AU - Camacho-Nieto, Oscar
PY - 2008
Y1 - 2008
N2 - The Lernmatrix, which is the first known model of associative memory, is a heteroassociative memory that can easily work as a binary pattern classifier if output patterns are appropriately chosen. However, this mathematical model undergoes fundamental patterns misclassification whenever crossbars saturation occurs. In this paper, a novel algorithm that overcomes Lernmatrix weaknesses is proposed. The crossbars saturation occurrence is solved by means of a dynamic threshold value which is computed for each recalled pattern. The algorithm applies the dynamic threshold value over the ambiguously recalled class vector in order to obtain a sentinel vector which is used for uncertainty elimination purposes. The efficiency and effectiveness of our approach is demonstrated through comparisons with other methods using real-world data.
AB - The Lernmatrix, which is the first known model of associative memory, is a heteroassociative memory that can easily work as a binary pattern classifier if output patterns are appropriately chosen. However, this mathematical model undergoes fundamental patterns misclassification whenever crossbars saturation occurs. In this paper, a novel algorithm that overcomes Lernmatrix weaknesses is proposed. The crossbars saturation occurrence is solved by means of a dynamic threshold value which is computed for each recalled pattern. The algorithm applies the dynamic threshold value over the ambiguously recalled class vector in order to obtain a sentinel vector which is used for uncertainty elimination purposes. The efficiency and effectiveness of our approach is demonstrated through comparisons with other methods using real-world data.
KW - Associative memories
KW - Dynamic threshold
KW - Lernmatrix
KW - Pattern classification
KW - Supervised learning
UR - http://www.scopus.com/inward/record.url?scp=55349136625&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-85920-8_55
DO - 10.1007/978-3-540-85920-8_55
M3 - Contribución a la conferencia
SN - 3540859195
SN - 9783540859192
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 445
EP - 452
BT - Progress in Pattern Recognition, Image Analysis and Applications - 13th Iberoamerican Congress on Pattern Recognition, CIARP 2008, Proceedings
T2 - 13th Iberoamerican Congress on Pattern Recognition, CIARP 2008
Y2 - 9 September 2008 through 12 September 2008
ER -