Year of Graduation
On the Acceleration of SGD for Mixture Separation Learning
In this work, we consider a binary classification problem in the setting of mixture of two probability distributions, such as they are similar on the most part of their support. We present a modification of importance sampling method for stochastic gradient descent. We provide empirical and theoretical analysis and conclude that our approach is rather more effective than classical stochastic gradient descent.