This paper proposes a general framework for the development of a novel approach to pattern recognition which is strongly based on graphical data types. These data keep at the same time the highly structured representation of classical syntactic and structural approaches and the subsymbolic capabilities of decision-theoretic approaches, typical of connectionist and statistical models. Like for decision-theoretic models, the recognition ability is mainly gained on the basis of learning from examples, that, however, are strongly structured.

Adaptive graphical pattern recognition beyond connectionist-based approaches / Adorni, G.; Cagnoni, Stefano; Gori, M.. - STAMPA. - (2000), pp. 17-27. (Intervento presentato al convegno Joint IAPR Int Wshop on Syntactical and Structural Pattern Recognition SSPR 2000 tenutosi a Alicante, Spain nel August/September 2000).

Adaptive graphical pattern recognition beyond connectionist-based approaches

CAGNONI, Stefano;
2000-01-01

Abstract

This paper proposes a general framework for the development of a novel approach to pattern recognition which is strongly based on graphical data types. These data keep at the same time the highly structured representation of classical syntactic and structural approaches and the subsymbolic capabilities of decision-theoretic approaches, typical of connectionist and statistical models. Like for decision-theoretic models, the recognition ability is mainly gained on the basis of learning from examples, that, however, are strongly structured.
2000
Adaptive graphical pattern recognition beyond connectionist-based approaches / Adorni, G.; Cagnoni, Stefano; Gori, M.. - STAMPA. - (2000), pp. 17-27. (Intervento presentato al convegno Joint IAPR Int Wshop on Syntactical and Structural Pattern Recognition SSPR 2000 tenutosi a Alicante, Spain nel August/September 2000).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/1450181
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