No. By linear SVM, I mean SVM that does not use the kernel trick. This is like logistic regression SGD with a different gradient function. Same idea otherwise. Yes. This is a convex problem.
On Wed, Feb 29, 2012 at 10:50 PM, Aditya Sarawgi <[email protected]>wrote: > So if I understand correctly, I think you mean that instead of having > multiple layers of svm > I just have 1 layer that gets the svm of the individual datasets and in > the reducer I get the > optimal of all. But is it guaranteed to give a global optima ? > > On Thu, Mar 1, 2012 at 1:37 AM, Ted Dunning <[email protected]> wrote: > >> For linear SVM, gradient descent is a fine algorithm. If you go into this >> work, I would recommend that you implement an all-reduce operation since >> iterated map-reduce is very inefficient. >> >> On Wed, Feb 29, 2012 at 10:30 PM, Aditya Sarawgi >> <[email protected]>wrote: >> >> > Hi, >> > >> > Thanks Todd for the pointer. I actually had one more paper in mind, and >> its >> > from >> > the original author of SVM >> > http://leon.bottou.org/publications/pdf/nips-2004c.pdf >> > >> > I think this makes more sense for mapreduce. I am open to other >> suggestions >> > or >> > algorithms. >> > >> > Thanks >> > Aditya Sarawgi >> > >> > On Thu, Mar 1, 2012 at 1:04 AM, Todd Johnson <[email protected]> >> > wrote: >> > >> > > The authors of that paper don't believe their algorithm is a good >> > candidate >> > > for mapreduce. See: >> > > >> > >> http://groups.google.com/group/psvm/browse_thread/thread/cedd3a6caef0f9c9# >> > > >> > > todd. >> > > >> > > >> > > >> > > On Wed, Feb 29, 2012 at 9:31 PM, Aditya Sarawgi < >> > [email protected] >> > > >wrote: >> > > >> > > > Hello, >> > > > >> > > > I am looking to implement psvm for Mahout as a part of of my >> > coursework. >> > > > The reference paper is >> > > > http://books.nips.cc/papers/files/nips20/NIPS2007_0435.pdf >> > > > and there is a implementation over >> http://code.google.com/p/psvm/which >> > > > uses MPI. >> > > > Any ideas, pointers are much appreciated. >> > > > >> > > > Thanks >> > > > Aditya Sarawgi >> > > > >> > > >> > >> > >> > >> > -- >> > Cheers, >> > Aditya Sarawgi >> > >> > > > > -- > Cheers, > Aditya Sarawgi >
