CLASSIFICATION AND PREDICTION - Universität Wien

Peter Brezany Institut für Softwarewissenschaft, WS 2002 2
Introduction (2)
People are good at generalizing from experience.
Computers usually excel at following explicit instructions over and over.
Slide 3
NN bridge this gap by modeling on a computer, the neural connections in human brains.
Their ability to generalize and learn from data mimics our own ability to learn from
experience.
This ability is useful for data mining.
Drawback: The results of training a NN are internal weights distributed throughout the
network. These weights provide no more insight into why the solution is valid than asking
many human experts why a particular decision is the right decision. They just know that it
is.
A Bit of History
1940 (neurologist Warren McCulloch and logician Walter Pits) – the original work on how
neurons work (no digital computers available at that time)
Slide 4
1950s - computer scientists implemented models called perceptrons based on the work of
McCulloch and Pits - some limited successes with perceptrons in the laboratory, but the
results were disappointing for general problem-solving. One of the reasons: there were no
powerful computers available
1970s - the study of NN implementations on computers slowed down drastically.
1982 - John Hopfield invented backpropagation, a way of training NN – a renaissance in NN
research.
1980s - research moved from the labs into the commercial world.
NN have been applied in virtually every industry field.