Discovering data structures using meta-learning, visualization and constructive neural networks

Abstract

Several visualization methods have been used to reveal hidden data structures, facilitating discovery of simplest data models. Insights gained in this way are used to create constructive neural networks implementing appropriate transformations that provide simplest models of data. This is an efficient approach to meta-learning, guiding the search for best models in the space of all data transformations. It can solve problems with complex inherent logical structure that are very difficult for traditional machine learning algorithms. © 2010 Springer-Verlag Berlin Heidelberg.