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Proceedings Paper

RAM-based neural networks for data mining applications
Author(s): Kenneth I. Agehed; Age J. Eide; Thomas Lindblad; Clark S. Lindsey; Geza Szekely; Joakim T. A. Waldemark; Karina E. Waldemark
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Paper Abstract

We discuss possible new hardware and software techniques for handling very large databases such as image archives. In particular, we investigate how high capacity solid-state `disks' could be used to speed the database processing by algorithms that require considerably memory space. One such algorithm, for example, called the RAM neural network, or weightless neural network, needs a number of large lookup tables to perform most efficiently. The solid state disks could provide fast storage both for the algorithm and the data. We also briefly discuss development of an algorithm to cluster images of similar objects. This algorithm could also benefit from a large cache of fast memory storage.

Paper Details

Date Published: 22 March 1999
PDF: 8 pages
Proc. SPIE 3728, Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks, (22 March 1999); doi: 10.1117/12.343067
Show Author Affiliations
Kenneth I. Agehed, Royal Institute of Technology (Sweden)
Age J. Eide, Royal Institute of Technology (Norway)
Thomas Lindblad, Royal Institute of Technology (Sweden)
Clark S. Lindsey, Royal Institute of Technology (Sweden)
Geza Szekely, Royal Institute of Technology (Sweden)
Joakim T. A. Waldemark, Royal Institute of Technology (Sweden)
Karina E. Waldemark, Royal Institute of Technology (Sweden)


Published in SPIE Proceedings Vol. 3728:
Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks
Thomas Lindblad; Mary Lou Padgett; Jason M. Kinser, Editor(s)

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