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Is
there any hope for learning functions that are
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not
linearly separable?
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Yes,
but a perceptron network isn't enough.
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One
needs more than one layer of units between
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inputs
and outputs to compute other functions.
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With
enough "hidden" units (units within), any
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boolean
function is computable, and any
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continuous
function is approximable.
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