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The ultimate goal of machines is to help humans to solve
problems.
Such problems range between two extremes: structured problems for
which the solution is totally defined (and thus are easily
programmed by humans), and random problems for which the solution
is completely undefined (and thus cannot be programmed). Problems
in the vast middle ground have solutions that cannot be well
defined and are, thus, inherently hard to program. Machine Learning
is the way to handle this vast middle ground, so that many tedious
and difficult hand-coding tasks would be replaced by automatic
learning methods. There are several machine learning tasks, and
this work is focused on a major one, which is known as
classification. Some classification problems are hard to solve, but
we show that they can be decomposed into much simpler sub-problems.
We also show that independently solving these sub-problems by
taking into account their particular demands, often leads to
improved classification performance.
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