The algorithm is as follows, where E p f, is the empirical expected value of J and E pfi is the expected value according to model p: • Set Az " equal to some arbitrary value, say: • Repeat until convergence: where (t) is the iteration index and the constant C is defined as follows: In practice C is maximised over the (x, y) pairs in the training data, although in theory C can be any constant greater than or equal to the figure in (8).
since determines the rate of convergence of the algorithm, it is preferable to keep C as small as possible.