What Size Net Gives Valid Generalization?

  title={What Size Net Gives Valid Generalization?},
  author={Eric B. Baum and David Haussler},
  journal={Neural Computation},
We address the question of when a network can be expected to generalize from m random training examples chosen from some arbitrary probability distribution, assuming that future test examples are drawn from the same distribution. Among our results are the following bounds on appropriate sample vs. network size. Assume 0 < ∊ 1/8. We show that if m O(W/∊ log N/∊) random examples can be loaded on a feedforward network of linear threshold functions with N nodes and W weights, so that at least a… CONTINUE READING
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