Supervision

Roughly, one can divide the learning procedures of most learning systems, including artificial neural networks, into supervised or unsupervised learning. In the case of supervised learning, a set of training samples is used. When no 'book of answers' is present, training is unsupervised.

For instance, attempting to predict tomorrow's Dow-Jones index using as input variations in interest rates certain key numbers of the largest companies, and the amount on transpiration present on Mr Greenspans' hands, would be a case of supervised learning. We have both input data and target data.

If we instead present a large number of spoken Finnish words to a network, and modify the network slightly according to some local, competitive rules with each new word, we end up with neurons that each recognise one Finnish phoneme (a dinstict unit of sound which separates words). This is a case of unsupervised learning; no-one told the network what phonemes there are - it found them itself. The unsupervised case includes the many interesting self-organisational techniques. Self organisation, although being mathematically rather simple, is robust and seems to be frequently employed in carving the layout of several systems in at least mammal wetware networks, including hearing, vision and language processing. We may have discovered these principles, but they probably played a key role in turning us into what we are, opposing the forces of increasing entropy by creating global order out of local interactions4.