A General Framework for Adaptive Processing of Data Structures

A General Framework for Adaptive Processing of Data Structures (1998)

by Paolo Frasconi , Marco Gori , Alessandro Sperduti

Venue:	IEEE TRANSACTIONS ON NEURAL NETWORKS
Citations:	105 - 44 self

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Datum	Value	Source
TITLE	A General Framework for Adaptive Processing of Data Structures	user correction - Legacy Corrections
AUTHOR NAME	Paolo Frasconi	user correction
AUTHOR NAME	Marco Gori	user correction
AUTHOR NAME	Alessandro Sperduti	user correction
ABSTRACT	A structured organization of information is typically required by symbolic processing. On the other hand, most connectionist models assume that data are organized according to relatively poor structures, like arrays or sequences. The framework described in this paper is an attempt to unify adaptive models like artificial neural nets and belief nets for the problem of processing structured information. In particular, relations between data variables are expressed by directed acyclic graphs, where both numerical and categorical values coexist. The general framework proposed in this paper can be regarded as an extension of both recurrent neural networks and hidden Markov models to the case of acyclic graphs. In particular we study the supervised learning problem as the problem of learning transductions from an input structured space to an output structured space, where transductions are assumed to admit a recursive hidden statespace representation. We introduce a graphical formalism for r...	user correction - Legacy Corrections
YEAR	1998	INFERENCE
VENUE	IEEE TRANSACTIONS ON NEURAL NETWORKS	user correction
VENUE TYPE	JOURNAL	INFERENCE
PAGES	768--786	INFERENCE
VOLUME	9	INFERENCE
CITATIONS	60 found	ParsCit 1.0