It was shown that over-training occurs when a classification boundary takes a severely distorted shape, from an otherwise optimal one, to accommodate sometimes few and isolated large errors in classification. Its dependence on the number of patterns in the training data set and the number of units in the network was demonstrated. It was shown that fewer training patterns and an increased number of units increase the severity of classification-boundary distortion. Vertical axis is Probability of Detection, while horizonatl axis is False Alarm Rate. The curves are generated by varying the alarm threshold.
Higher curves are better.
The project is complete.