4. LEARNING TASKS
The choice of a particular learning algorithm is influenced by the learning task that an ANN is required to perform. We identify six basic learning tasks that apply to the use of different artificial neural networks.
Pattern Association
Association has been known to be a prominent feature of human memory since Aristotle, and all models of cognition use association in one form or the other as the basic operation. Association takes one of two forms: autoassociation or heteroassociation. In autoassociation, an ANN is required to store a set of patterns by repeatedly presenting them to the network. The network is subsequently presented with a partial description or a distorted, noisy version of an original pattern, and the task is to retrieve and recall that particular pattern. Heteroassociation differs from autoassociation in that an arbitrary set of input patterns is paired with another arbitrary set of output patterns. Autoassociation involves the use of unsupervised learning, whereas heteroassociation learning is supervised. For both, autoassociation and heteroassociation, there are two main phases in the application of an ANN for pattern-association problems:
1. The storage phase, which refers to the training of the network in accordance with given patterns, and
2. The recall phase, which involves the retrieval of a memorized pattern in response to the presentation of a noisy or distorted version of a key pattern to the network.