  We present an application of machine-learning (ML) techniques to source
selection in the optical transient survey data with Hyper Suprime-Cam (HSC) on
the Subaru telescope. Our goal is to select real transient events accurately
and in a timely manner out of a large number of false candidates, obtained with
the standard difference-imaging method. We have developed the transient
selector which is based on majority voting of three ML machines of AUC
Boosting, Random Forest, and Deep Neural Network. We applied it to our
observing runs of Subaru-HSC in 2015 May and August, and proved it to be
efficient in selecting optical transients. The false positive rate was 1.0% at
the true positive rate of 90% in the magnitude range of 22.0--25.0 mag for the
former data. For the latter run, we successfully detected and reported ten
candidates of supernovae within the same day as the observation. From these
runs, we learned the following lessons: (1) the training using artificial
objects is effective in filtering out false candidates, especially for faint
objects, and (2) combination of ML by majority voting is advantageous.
