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Networks learn to identify
whether a given pattern falls inside a class that has a two-dimensional
uniform distribution. There are 2 linear inputs and 1 sigmoid output unit
that indicates whether a point is inside or outside a class of a particular
distribution with a given shape, size, and position. The network is trained
with 225 patterns forming a 15 x 15 grid covering whole input space. The blue
points are training patterns inside the target shape. Red points are training
patterns outside the target shape. There are 200 randomly determined test
patterns uniformly distributed over the input space, making a fine grid of
220 x 220 input patterns. White indicates test points inside the target
shape, black outside, and gray uncertain.
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