| 摘要 |
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.
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