OPTIMAL CESSATION OF TRAINING AND ASSESSMENT OF ACCURACY IN A GIVEN CLASS OF NEURAL NETWORKS

A system, method, and process for configuring iterative, self-correcting algorithms, such as neural networks, so that the weights or characteristics to which the algorithm converge to do not require the use of test or validation sets, and the maximum error in failing to achieve optimal cessation of training can be calculated. In addition, a method for internally validating the correctness, i.e. determining the degree of accuracy of the predictions derived from the system, method, and process of the present invention is disclosed.