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Aspects of discrete mathematics and probability in the theory of machine learning
Martin Anthony
Discrete Applied Mathematics
Volume 156,
Number 6,
,
2008.
AbstractThis paper concerns developments
in the use of probabilistic and combinatorial techniques in the
analysis of machine learning. Initially, probabilistic models of
learning addressed binary classification (or pattern
classification). More recently, analysis has been extended to
regression problems, and to classification problems in which the
classification is achieved by using real-valued functions (where
the concept of a large margin has proven useful). Another
development, important in obtaining more applicable models, has
been the derivation of data-dependent bounds. Here, we discuss
some of the main probabilistic and combinatorial techniques and
results. [Edit]
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