Absolutely. I will read through. The idea is to first fix the learning rate update equation in OLR. I think this code in OnlineLogisticRegression is the current equation ?
@Override public double currentLearningRate() { return mu0 * Math.pow(decayFactor, getStep()) * Math.pow(getStep() + stepOffset, forgettingExponent); } I presume that you would like Adagrad-like solution to replace the above ? On Wed, Nov 27, 2013 at 8:18 PM, Ted Dunning <ted.dunn...@gmail.com> wrote: > On Wed, Nov 27, 2013 at 7:07 AM, Vishal Santoshi < > vishal.santo...@gmail.com> > > > > > > > Are we to assume that SGD is still a work in progress and > implementations ( > > Cross Fold, Online, Adaptive ) are too flawed to be realistically used ? > > > > They are too raw to be accepted uncritically, for sure. They have been > used successfully in production. > > > > The evolutionary algorithm seems to be the core of > > OnlineLogisticRegression, > > which in turn builds up to Adaptive/Cross Fold. > > > > >>b) for truly on-line learning where no repeated passes through the > data.. > > > > What would it take to get to an implementation ? How can any one help ? > > > > Would you like to help on this? The amount of work required to get a > distributed asynchronous learner up is moderate, but definitely not huge. > > I think that OnlineLogisticRegression is basically sound, but should get a > better learning rate update equation. That would largely make the > Adaptive* stuff unnecessary, expecially if OLR could be used in the > distributed asynchronous learner. >