Learning Rules
A learning rule is defined as a procedure for modifying the weights and biases of a network. (This procedure can also be referred to as a training algorithm.) The learning rule is applied to train the network to perform some particular task. Learning rules in this toolbox fall into two broad categories: supervised learning, and unsupervised learning.
In supervised learning, the learning rule is provided with a set of examples (the training set) of proper network behavior
where 
is an input to the network, and 
is the corresponding correct (target) output. As the inputs are applied to the network, the network outputs are compared to the targets. The learning rule is then used to adjust the weights and biases of the network in order to move the network outputs closer to the targets. The perceptron learning rule falls in this supervised learning category.
In unsupervised learning, the weights and biases are modified in response to network inputs only. There are no target outputs available. Most of these algorithms perform clustering operations. They categorize the input patterns into a finite number of classes. This is especially useful in such applications as vector quantization.
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