On learning ?-perceptron networks on the uniform distribution

  title={On learning ?-perceptron networks on the uniform distribution},
  author={Mostefa Golea and Mario Marchand and Thomas R. Hancock},
  journal={Neural Networks},
We investigate the learnability, under the uniform distribution, of neural concepts that can be represented as simple combinations of nonoverlapping perceptrons (also called μ perceptrons) with binary weights and arbitrary thresholds. Two perceptrons are said to be nonoverlapping if they do not share any input variables. Specifically, we investigate, within the distribution-specific PAC model, the learnability of μ perceptron unions, decision lists , and generalized decision lists . In contrast… CONTINUE READING