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Read Chapter 4 of Mitchell.
``ANNs are particularly suited to learning in problems where the
training data corresponds to noisy, complex sensor data, such as
inputs from cameras and microphones.'' (p.83)
Think about using ANNs especially when
- Target function is defined over input instances that can be
represented as a vector of predefined features.
- Target function is a vector of real values (Note that discrete
valued target functions are just a special case of this because we can
always partition continuous space into discrete intervals).
- Training examples may contain noise/errors.
- Long training times are acceptable.
- Fast evaluation of target function is required.
- Human understandability of the learned target function is of low
priority, i.e. we don't care how the problem is solved as long as it