We present an overview of current research on
artificial neural networks, emphasizing a statistical perspective.
We view neural networks as parameterized graphs that make
probabilistic assumptions about data, and view learning algorithms
as methods for finding parameter values that look probable in the
light of the data. We discuss basic issues in representation and
learning, and treat some of the practical issues that arise in
fitting networks to data. We also discuss links between neural
networks and the general formalism of graphical
models.
