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)
