One of the most significant attributes of a neural
network is its ability to learn by interacting with its environment
or with an information source. Learning in a neural network is
normally accomplished through an adaptive procedure, known as
a learning rule or algorithm whereby the weights of the network
are incrementally adjusted so as to improve a predefined performance
measure over time.
In the context of artificial neural networks, the
process of learning is best viewed as an optimization process.
More precisely, the learning process can be viewed as "search"
in a multi-dimensional parameter (weight) space for a solution,
which gradually optimizes a prespecified objective (criterion)
function. This view is adopted in this chapter, and it allows
us to unify a wide range of existing learning rules, which otherwise
would have looked more like a diverse variety of learning procedures.
This chapter presents a number of basic learning
rules for supervised, reinforced, and unsupervised learning tasks.
In supervised learning (also known as learning with a teacher
or associative learning), each input pattern/signal received from
the environment is associated with a specific desired target pattern.
Usually, the weights are synthesized gradually, and at each step
of the learning process they are updated so that the error between
the network's output and a corresponding desired target is reduced.
On the other hand, unsupervised learning involves the clustering
of (or the detection of similarities among) unlabeled patterns
of a given training set. The idea here is to optimize (maximize
or minimize) some criterion or performance function defined in
terms of the output activity of the units in the network. Here,
the weights and the outputs of the network are usually expected
to converge to representations which capture the statistical regularities
of the input data. Reinforcement learning involves updating the
network's weights in response to an "evaluative" teacher
signal; this differs from supervised learning, where the teacher
signal is the "correct answer". Reinforcement learning
rules may be viewed as stochastic search mechanisms that attempt
to maximize the probability of positive external reinforcement
for a given training set.
In most cases, these learning rules are presented
in the basic form appropriate for single unit training. Exceptions
are cases involving unsupervised (competitive or feature mapping)
learning schemes where an essential competition mechanism necessitates
the use of multiple units. For such cases, simple single layer
architectures are assumed. Later chapters of this book (Chapters
5, 6, and 7) extend some of the learning rules discussed here
to networks with multiple units and multiple layers.
