  Several variants of a stochastic local search process for constructing the
synaptic weights of an Ising perceptron are studied. In this process, binary
patterns are sequentially presented to the Ising perceptron and are then
learned as the synaptic weight configuration is modified through a chain of
single- or double-weight flips within the compatible weight configuration space
of the earlier learned patterns. This process is able to reach a storage
capacity of $\alpha \approx 0.63$ for pattern length N = 101 and $\alpha
\approx 0.41$ for N = 1001. If in addition a relearning process is exploited,
the learning performance is further improved to a storage capacity of $\alpha
\approx 0.80$ for N = 101 and $\alpha \approx 0.42$ for N=1001. We found that,
for a given learning task, the solutions constructed by the random walk
learning process are separated by a typical Hamming distance, which decreases
with the constraint density $\alpha$ of the learning task; at a fixed value of
$\alpha$, the width of the Hamming distance distributions decreases with $N$.
