Associative memory design using support vector machines.
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.
Casali
Daniele
D
Department of Electronic Engineering, University of Rome "Tor Vergata," 00100 Rome, Italy. daniele.casali@uniroma2.it
Costantini
Giovanni
G
Perfetti
Renzo
R
Ricci
Elisa
E
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003198:Computer Simulation; D003205:Computing Methodologies; D016247:Information Storage and Retrieval / Q000379:methods*; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*