Formal determination of context in contextual recursive cascade correlation networks

Authors:
Alessio Micheli;Diego Sona;Alessandro Sperduti
Affiliations:
Dipartimento di Informatica, Università di Pisa, Italy;ITC-IRST, Trento, Italy;Dipartimento di Matematica Pura ed Applicata, Università di Padova, Italy
Venue:
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Year:
2003

Citing 5
Cited 2

Bi-directional computing architecture for time series prediction

Neural Networks
Extended Cascade-Correlation for Syntactic and Structural Pattern Recognition

SSPR '96 Proceedings of the 6th International Workshop on Advances in Structural and Syntactical Pattern Recognition
Bi-Causal Recurrent Cascade Correlation

IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
Supervised neural networks for the classification of structures

IEEE Transactions on Neural Networks
A general framework for adaptive processing of data structures

IEEE Transactions on Neural Networks

Universal Approximation Capability of Cascade Correlation for Structures

Neural Computation
Graph self-organizing maps for cyclic and unbounded graphs

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

We consider the Contextual Recursive Cascade Correlation model (CRCC), a model able to learn contextual mappings in structured domains. We propose a formal characterization of the "context window", i.e., given a state variable, the "context window" is the set of state variables that directly or indirectly contribute to its determination. On the basis of this definition, a formal and compact expression describing the "context windows" for the CRCC, and RCC model, are derived.