  In this Letter, we present a new strategy for applying the learning machine
to study phase transitions. We train the learning machine with samples only
obtained at a non-critical parameter point, aiming to establish intrinsic
correlations between the learning machine and the target system. Then, we find
that the accuracy of the learning machine, which is the most important
performance index in conventional learning machines, is no longer a key goal of
the training in our approach. Instead, relatively low accuracy of identifying
unlabeled data category can help to determine the critical point with greater
precision, manifesting the singularity around the critical point. It thus
provides a robust tool to study the phase transition. The classical
ferromagnetic and percolation phase transitions are employed as illustrative
examples.
