sample.txt.txt

A third important attribute of the training experience is how well it repre-
sents the distribution of examples over which the final system performance P must
be measured. In general, learning is most reliable when the training examples fol-
low a distribution similar to that of future test examples. In our checkers learning
scenario, the performance metric P is the percent of games the system wins in
the world tournament. If its training experience E consists only of games played
against itself, there is an obvious danger that this training experience might not
be fully representative of the distribution of situations over which it will later be
tested. For example, the learner might never encounter certain crucial board states
that are very likely to be played by the human checkers champion. In practice,
it is often necessary to learn from a distribution of examples that is somewhat
different from those on which the final system will be evaluated (e.g., the world
checkers champion might not be interested in teaching the program!). Such situ-
ations are problematic because mastery of one distribution of examples will not
necessary lead to strong performance over some other distribution. We shall see
that most current theory of machine learning rests on the crucial assumption that
the distribution of training examples is identical to the distribution of test ex-
amples. Despite our need to make this assumption in order to obtain theoretical
results, it is important to keep in mind that this assumption must often be violated
in practice.
To proceed with our design, let us decide that our system will train by
playing games against itself. This has the advantage that no external trainer need
be present, and it therefore allows the system to generate as much training data
as time permits. We now have a fully specified learning task