An Innovative Way to Measure the Quality of a Neural Network Without the Use of a Test Set
Giovanni Pilato*, Filippo Sorbello* and Giorgio Vassallo**
Department of Automata and Informatics Engineering University of Palermo Viale delle Scienze, Palermo, Italy CRES Centro per la Ricerca Elettronica in Sicilia Via Regione Siciliana, Monreale (PA), Italy
In this paper, three quality factors are introduced in order to measure the quality of a neural network. Each factor deals with a particular feature of quality: the ability of the network in learning training set samples; generalization capability related to the gradient, in the nearby of the training patterns, of the network output function; the computational cost of the architecture during the production phase, related to the number of connections between neural units. The validity of the proposed solution has been tested using three well-known benchmarks. Experimental results show that quality factors introduced in this paper can be a valid alternative to the test set method.