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Proceedings Paper

Dynamic perceptron: some theorems about the possibility of parallel pattern recognition with an application to high-energy physics
Author(s): Antonio Luigi Perrone; Patrizia Castiglione; Gianfranco Basti; Roberto Messi
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Paper Abstract

In the context of M. Minsky's and S. Papert's theorems on the impossibility of evaluating simple linear predicates by parallel architectures we want to show how these limitations can be avoided by introducing a generalized input-dependent preprocessing technique that does not suppose any a-priori knowledge of input like in classical input filtering procedures. This technique can be formalized in a very general way and can be also deduced by meta- mathematical arguments. A further development of the same technique can be applied at level of learning procedure to introduce in such a way the complete notion of `dynamic perception'. From the experimental standpoint, we show two applications of the dynamic perceptron in particle track recognition in high-energy accelerators. Firstly, we show the amazing improvement of performances that can be obtained in a perceptron architecture with classical learning by adding our dynamic preprocessing technique, already introduced last year in another paper presented at this Conference. Secondly, we show the first results of this technique extended also at the level of learning procedure always applied to the problem of particle track recognition.

Paper Details

Date Published: 2 March 1994
PDF: 12 pages
Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); doi: 10.1117/12.170003
Show Author Affiliations
Antonio Luigi Perrone, Univ. di Roma Tor Vergata and INFN (Italy)
Patrizia Castiglione, Univ. di Roma La Sapienza (Italy)
Gianfranco Basti, Pontifical Gregorian Univ. and INFN (Italy)
Roberto Messi, Univ. di Roma Tor Vergata and INFN (Italy)


Published in SPIE Proceedings Vol. 2243:
Applications of Artificial Neural Networks V
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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