Upper and lower bounds for the typical storage capacity of a constructive
algorithm, the Tilinglike Learning Algorithm for the Parity Machine [M. Biehl
and M. Opper, Phys. Rev. A {\bf 44} 6888 (1991)], are determined in the
asymptotic limit of large training set sizes. The properties of a perceptron
with threshold, learning a training set of patterns having a biased
distribution of targets, needed as an intermediate step in the capacity
calculation, are determined analytically. The lower bound for the capacity,
determined with a cavity method, is proportional to the number of hidden units.
The upper bound, obtained with the hypothesis of replica symmetry, is close to
the one predicted by Mitchinson and Durbin [Biol. Cyber. {\bf 60} 345 (1989)].Comment: 13 pages, 1 figur