Dealing with structured data has always represented a huge problem for classical neural methods. Although many efforts have been performed, they usually pre-process data and then use classic machine learning algorithm. Another problem that machine learning algorithm have to face is the intrinsic uncertainty of data, where in such situations classic algorithm do not have the means to handle them. In this work a novel neuro-fuzzy model for structured data is presented that exploits both neural and fuzzy methods. The proposed model called Fuzzy Graph Neural Network (F-GNN) is based on GNN, a model able to handle structure data. A proof of F-GNN approximation properties is provided together with a training algorithm.
A Neuro Fuzzy Approach for Handling Structured Data
FERONE, Alessio;PETROSINO, Alfredo
2008-01-01
Abstract
Dealing with structured data has always represented a huge problem for classical neural methods. Although many efforts have been performed, they usually pre-process data and then use classic machine learning algorithm. Another problem that machine learning algorithm have to face is the intrinsic uncertainty of data, where in such situations classic algorithm do not have the means to handle them. In this work a novel neuro-fuzzy model for structured data is presented that exploits both neural and fuzzy methods. The proposed model called Fuzzy Graph Neural Network (F-GNN) is based on GNN, a model able to handle structure data. A proof of F-GNN approximation properties is provided together with a training algorithm.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
