 For each possible parent, each child computes a prediction vector by multiplying its output by a weight matrix (trained by backpropagation).Next the output of the parent is computed as the scalar product of a prediction with a coefficient representing the probability that this child belongs to that parent.A child whose predictions are relatively close to the resulting output successively increases the coefficient between that parent and child and decreases it for parents that it matches less well.