Using phidelta diagrams to discover relevant patterns in multilayer perceptrons.

ABSTRACT: Understanding the inner behaviour of multilayer perceptrons during and after training is a goal of paramount importance for many researchers worldwide. This article experimentally shows that relevant patterns emerge upon training, which are typically related to the underlying problem difficulty. The occurrence of these patterns is highlighted by means of [Formula: see text] diagrams, a 2D graphical tool originally devised to support the work of researchers on classifier performance evaluation and on feature assessment. The underlying assumption being that multilayer perceptrons are powerful engines for feature encoding, hidden layers have been inspected as they were in fact hosting new input features. Interestingly, there are problems that appear difficult if dealt with using a single hidd

SUBMITTER: Armano G 

PROVIDER: S-EPMC7721750 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature