A Machine Learning Approach for Abstraction based on the Idea of Deep Belief Artificial Neural Networks
Florian Neukart, Sorin-Aurel Moraru · Procedia Engineering · 2014
In a time-critical world knowledge at the right time might decide everything. However, storing data does not correspond with understanding the knowledge it contains. Thus, solutions capable of learning problem statements and gathering knowledge from huge amounts of data, be it structured or unstructured, are required. This is where computational intelligence and the introduced approach apply: within this paper, a new method of combining restricted Boltzmann machines and feed forward artificial neural networks is elucidated as well as the accuracy of the resulting solution is proofed.