Multiple neural networks and weighted voting

Proposes to train a number of neural networks independently, either with different learning algorithms or with the same algorithm but with different sets of parameters and then take a vote over them in the form of a weighted majority. All the nets learn essentially the same task but converge to different solutions due to different learning parameter values, e.g. net structure. The voting schemes investigated are static vs. dynamic, where in dynamic voting scheme, the network complexity is also taken into account. The system assigns a weight of `confidence´ to each participating net proportional to the net´s success and complexity. An empirical work shows that having multiple nets significantly improves generalization, i.e., higher success is achieved in classifying previously unseen data. This framework is not limited to neural nets but can be applied to learning systems in general