As a result, representational resources may be wasted on areas of the input space that are irrelevant to the learning task.

In regression applications they can be competitive when the dimensionality of the input space is relatively small.

The input space is given by real numbers.

The data lie approximately on a manifold of much lower dimension than the input space.

Sampling (running) the model at a number of points in its input space.

A third issue is the dimensionality of the input space.

The transform, , defines a search curve in the input space.

The other way is to think of neuronal weights as pointers to the input space.

The set of all possible input states is called as input space.

For example, a series of similarity relations between the input spaces can be compressed into a single identity relationship in the blend.