Apart from the obvious technical generalization of the likelihood function the weights could on aver-age be interpreted as a modification of the training set by adding new examples with intensity w(i) while keeping the attributes and the classes (x(i), y(i)).
for w(i) < 1 the ith example is deleted with probability 1 − w(i).