Linear algebra approach to neural associative memories and noise performance of neural classifiers

 
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1991 (EN)
Linear algebra approach to neural associative memories and noise performance of neural classifiers (EN)

Βασιλάς, Νικόλαος (EL)
Fassett, K. (EN)
Cherkassky, Vladimir (EN)

Τεχνολογικό Εκπαιδευτικό Ίδρυμα Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Πληροφορικής Τ.Ε. (EL)

The authors present an analytic evaluation of saturation and noise performance for a large class of associative memories based on matrix operations. The importance of using standard linear algebra techniques for evaluating noise performance of associative memories is emphasized. The authors present a detailed comparative analysis of the correlation matrix memory and the generalized inverse memory construction rules for auto-associative memory and neural classifiers. Analytic results for the noise performance of neural classifiers that can store several prototypes in one class are presented. The analysis indicates that for neural classifiers the simple correlation matrix memory provides better noise performance than the more complex generalized inverse memory. (EN)

journalArticle

Γραμμική Άλγεβρα (EN)
Neural Associative Memories (EN)
Νευρωνικές συνειρμικές μνήμες (EN)
Linear algebra (EN)
Νευρωνικοί ταξινομητές (EN)
Noise Performance (EN)
Απόδοση θορύβου (EN)
Neural Classifiers (EN)

ΤΕΙ Αθήνας (EL)
Technological Educational Institute of Athens (EN)

IEEE Transactions on Computers (EN)

English

1991-12

DOI: 10.1109/12.106229

N/A (EN)



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