Emerging systems for the use of neural networks

Although an individual neural network has proven capabilities that are powerful for pattern detection and function approximation, real-life applications of neural networks often require an entire system for the training and usage of such neural networks. We describe systems for using neural networks in decision making roles such as medical diagnosis and pattern recognition. In our medical applications, the neural network output is treated as a composite variable subject to statistical validation such as an ROC plot analysis, use of re-sampled training to measure performance variance, and avoidance of overtraining. Another system for use of neural networks lies in our approach for training on boundaries rather than individual data points in pattern classification and image analysis problems. We discuss optimizing the neural network and training using these systems.