A Framework for Frugal Supervised Learning with Incremental Neural Networks

Abstract

This study proposes an implementation of an incremental neural network (INN) that was initially designed for affective computing tasks. INNs are a family of machine learning algorithms that combine prototype-based classifiers with neural networks. They achieve state-of-the-art performance with less data than traditional approaches. In this research, we conduct an in-depth review of INN mechanisms and present a research-grade framework that enables the use of INNs on arbitrary data. We evaluated our implementation on two different datasets, including the AVEC2014 Challenge, which involved predicting depressive state from auditive and visual modalities. Our results are encouraging, demonstrating the potential of INNs in situations where approaches have to be explainable or when data are scarce.