cool, thanks :) On Tue, Sep 20, 2011 at 11:10 PM, Hector Yee <[email protected]> wrote:
> Yeah its a two line change to PassiveAggressive.java (MAHOUT-702) > > change the loss to: > > loss = hinge ( | score - actual| - epsilon ) where hinge(x) = 0 if x < 0, x > otherwise > epsilon is a new param that controls how much error we tolerate > tau remains the same > delta = sign(actual - score) * tau * instance > > > On Tue, Sep 20, 2011 at 2:21 PM, Ted Dunning <[email protected]> > wrote: > > > Anything that requires the solution of large linear systems is usually > > susceptible to SGD approaches. > > > > On Tue, Sep 20, 2011 at 11:24 AM, deneche abdelhakim <[email protected] > > >wrote: > > > > > I was reading this paper: > > > > > > "Combining Predictions for Accurate Recommender Systems" > > > http://www.commendo.at/UserFiles/commendo/File/kdd2010-paper.pdf > > > > > > and one particular method used to blend different recommenders is KRR > > > (Kernel Ridge Regression). The authors had the followings conclusion > > about > > > it: > > > > > > "KRR is worse than neural networks, but the results are promising. An > > > increase of the training set size would lead to a more accurate model. > > But > > > the huge computational re- > > > quirements of KRR limits us to about 6% data. The train time for one > KRR > > > model on 6% subset (about 42000 samples) is 4 hours." > > > > > > I don't know why, but I really want to see the quality of the results > of > > > this method when using larger training sets. So my question is the > > > following: will such method benefit from a distributed version > > (mapreduce) > > > ? > > > is such thing already available ? is it interesting to the Mahout > project > > > in > > > general ? I started to document about it and it seems to require some > big > > > linear system solving. > > > > > > > > > -- > Yee Yang Li Hector <https://plus.google.com/106746796711269457249> > Professional Profile <http://www.linkedin.com/in/yeehector> > http://hectorgon.blogspot.com/ (tech + travel) > http://hectorgon.com (book reviews) >
