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Weight-space
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– |
Each
axis corresponds to a weight
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– |
A
point is a weight vector
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– |
Dimensionality
= #weights +1 extra dimension for the loss
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Data-space
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– |
Each
axis corresponds to an input value
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A
point is a data vector. A decision surface is a plane.
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Dimensionality
= dimensionality of a data vector
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“Case-space”
(used in Bishop figure 3.2)
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Each
axis corresponds to a training case
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A
point assigns a scalar value to every training case
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So it
can represent the 1-D targets or it can represent the
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value
of one input component over all the training data.
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Dimensionality
= #training cases
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