Finite Sum Acceleration vs. Adaptive Learning Rates for the Training of Kernel Machines on a Budget
نویسنده
چکیده
Training predictive models with stochastic gradient descent is widespread practice in machine learning. Recent advances improve on the basic technique in two ways: adaptive learning rates are widely used for deep learning, while acceleration techniques like stochastic average and variance reduced gradient descent can achieve a linear convergence rate. We investigate the utility of both types of methods as well as combinations thereof for the training of kernel machines on a budget.
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تاریخ انتشار 2016