An online Gaussian mixture learning model for dynamic data utilizes an
adaptive learning rate schedule to achieve fast convergence while
maintaining adaptability of the model after convergence. Experimental
results show an unexpectedly dramatic improvement in modeling accuracy
using an adaptive learning schedule.