For pattern recognition, the neural network architectures that can be applied are MLPs (supervised) and the Kohonen Network (unsupervised). In the first case, the problem should be set up as a classification problem, that is, the data should be transformed into the X-Y dataset, where for every data record in X there should be a corresponding class in Y. As stated in Chapter 3, Perceptrons and Supervised Learning and Chapter 6, Classifying Disease Diagnosis the output of the neural network for classification problems should have all of the possible classes, and this may require preprocessing of the output records.
For the other case, unsupervised learning, there is no need to apply labels to the output, but the input data should be properly structured. To remind you, the schema of both neural networks are shown in the next figure:
