# Can you perform stochastic learning followed by batch learning in neural networks?

I'm trying to teach myself about neural networks. I've been reading through the "Efficient BackProp" paper that's highly sited and it's brought me this question;

Since stochastic learning converges significantly faster than batch learning, but has a higher level of noise (doesn't converge to the global minimum) should one perform Stochastic learning until the network reaches a platue/minimum then use batch learning to finish the process?

When I say batch learning I mean treat the entire training set as a single batch (compute the sum of the derivatives of the weights for all samples and then update the network).