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| The inductive bias of a learning algorithm is the set of
assumptions that the learner uses to predict outputs given
inputs that it has not encountered (Mitchell, 1980). In machine
learning, one aims to construct algorithms that are able to
learn to predict a certain target output. To achieve this, the
learning algorithm is presented some training examples that
demonstrate the intended relation of input and output values.
Then the learner is supposed to approximate the correct output,
even for examples that have not been shown during training.
Without any additional assumptions, this task cannot be solved
exactly since unseen situations might have an arbitrary output
value. The kind of necessary assumptions about the nature of the
target function are subsumed in the term inductive bias
(Mitchell, 1980; desJardins and Gordon, 1995). A classical
example of an inductive bias is Occam's Raz