  Future electron-positron colliders will play a leading role in the precision
measurement of Higgs boson couplings which is one of the central interests in
particle physics. Aiming at maximizing the performance to measure the Higgs
couplings to the bottom, charm and strange quarks, we develop machine learning
methods to improve the selection of events with a Higgs decaying to dijets. Our
methods are based on the Boosted Decision Tree (BDT), Fully-Connected Neural
Network (FCNN) and Convolutional Neural Network (CNN). We find that the BDT and
FCNN-based algorithms outperform the conventional cut-based method. With our
improved selection of Higgs decaying to dijet events using the FCNN, the charm
quark signal strength is measured with a $16\%$ error, which is roughly a
factor of two better than the $34\%$ precision obtained by the cut-based
analysis. Also, the strange quark signal strength is constrained as $\mu_{ss}
\lesssim 35$ at the $95\%$ C.L. with the FCNN, which is to be compared with
$\mu_{ss} \lesssim 70$ obtained by the cut-based method.

High Energy Physics - Phenomenology;High Energy Physics - Experiment