[top of page 8 of handout]
CLASSIFICATION OF LEARNING MECHANISMS
– Auto Associator set of patterns repeatedly presented and system “stores” it ... “a pattern is associated with itself”. Then when part is given, the whole original is retrieved == Habituation or perceptual learning
– Pattern Associator pairs repeated. System learns that when one member is presented it is “supposed” to produce the other. “In this paradigm one seeks a mechanism in which an essentially abitrary set of input patterns can be paired with an abitrary set of output patterns. == Pavlovian conditioning, or stimulus-response motor habits.
– Classification paradigm this is a variant on the previous 2. There is a fixed set of categories into which the stimulus patterns are to be classified. There is a training session in which the system is presented with the stimulus patterns along with the categories to which each stimulus belongs. This is where the “perceptron convergence theorem is proved” == Discrimination Learning
– Regularity detector For a population of stimulus patterns each pattern S(k) is presented with a probability p(k). The system is supposed to discover statistically salient features of the input population. (cf Rescorla's conditional probability suggestions) and experiments on probability learning in animals. == probability learning and complex Pavlovian conditioning; discrimination of reward rates.