Michael Schmitt

The computational complexity of learning from binary examples is

investigated for linear threshold neurons. We introduce

combinatorial measures that create classes of infinitely many

learning problems with sample restrictions. We analyze how the

complexity of these problems depends on the values for the measures.

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