Associative Memory Design Using Support Vector Machines

Author

Casali, D. ; Costantini, G. ; Perfetti, R. ; Ricci, E.

Author_Institution

Dept. of Electron. Eng., Rome Univ.

Volume

17

Issue

5

fYear

2006

Firstpage

1165

Lastpage

1174

Abstract

The relation existing between support vector machines (SVMs) and recurrent associative memories is investigated. The design of associative memories based on the generalized brain-state-in-a-box (GBSB) neural model is formulated as a set of independent classification tasks which can be efficiently solved by standard software packages for SVM learning. Some properties of the networks designed in this way are evidenced, like the fact that surprisingly they follow a generalized Hebb´s law. The performance of the SVM approach is compared to existing methods with nonsymmetric connections, by some design examples

Keywords

content-addressable storage; recurrent neural nets; support vector machines; generalized brain-state-in-a-box neural model; recurrent associative memory design; support vector machines; Associative memory; Biological neural networks; Brain modeling; Design methodology; Multi-layer neural network; Network synthesis; Prototypes; Recurrent neural networks; Support vector machine classification; Support vector machines; Associative memories; brain-state-in-a-box neural model; support vector machines (SVMs); Algorithms; Artificial Intelligence; Cluster Analysis; Computer Simulation; Computing Methodologies; Information Storage and Retrieval; Models, Theoretical; Neural Networks (Computer); Pattern Recognition, Automated;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2006.877539

Filename

1687927