Modeling Affection Mechanisms using Deep and Self-Organizing Neural Networks

Abstract

One of the most important aspects of affective computing is how to make computational systems use emotion concepts in different situations. Although several types of research were done, we are still far away from having a system which can recognize and learn emotion concepts in a satisfactory way. In this thesis, we propose computational models which introduce a unified solution for emotional attention, recognition, and learning. These models are competitive in each of these tasks, and also provide an overview of a learning mechanism which adapts its knowledge according to a given situation.