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Your search keyword '"Casali D"' showing total 2 results
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2 results on '"Casali D"'

1. Associative memory design using support vector machines.

Author

Casali D, Costantini G, Perfetti R, and Ricci E

Subjects
Cluster Analysis, Computer Simulation, Neural Networks, Computer, Algorithms, Artificial Intelligence, Computing Methodologies, Information Storage and Retrieval methods, Models, Theoretical, Pattern Recognition, Automated methods
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.

Published
2006
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2. Associative memory design for 256 gray-level images using a multilayer neural network.

Author

Costantini G, Casali D, and Perfetti R

Subjects
Artificial Intelligence, Colorimetry methods, Computer Graphics, Computer Simulation, Numerical Analysis, Computer-Assisted, Algorithms, Image Interpretation, Computer-Assisted methods, Information Storage and Retrieval methods, Models, Theoretical, Neural Networks, Computer, Pattern Recognition, Automated methods, Signal Processing, Computer-Assisted
Abstract

A design procedure is presented for neural associative memories storing gray-scale images. It is an evolution of a previous work based on the decomposition of the image with 2L gray levels into L binary patterns, stored in L uncoupled neural networks. In this letter, an L-layer neural network is proposed with both intralayer and interlayer connections. The connections between different layers introduce interactions among all the neurons, increasing the recall performance with respect to the uncoupled case. In particular, the proposed network can store images with the commonly used number of 256 gray levels instead of 16, as in the previous approach.

Published
2006
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