An input data set compression method for improving the training ability of neural networks

Artificial Neural Networks (ANNs) can learn complex functions from the input data and are relatively easy to implement in any application. On the other hand, a significant disadvantage of their usage is they usually high training time-need, which scales with the structural parameters of the networks and the quantity of input data. However, this can be done offline; the training has a non-negligible cost and further, can cause a delay in the operation. To increase the speed of the training of the ANNs used for classification, we have developed a new training procedure: instead of directly using the training data in the training phase, the data is first clustered and the ANNs are trained by using only the centers of the obtained clusters (which are basically the compressed versions of the original input data).