You can do supervised learning by outputing the clusters and labeling them 0-9.
> On Jan 23, 2014, at 10:34 PM, Tharindu Rusira <[email protected]> > wrote: > > On Fri, Jan 24, 2014 at 9:50 AM, Angus Macnab <[email protected]>wrote: > >> This is a pretty classic machine learning problem and can be handled with >> several different algorithms. Logistic regression is the obvious choice, >> but clustering algorithms will work fine also. Just decompose the pixels >> into a really long vector and train your algorithm with the input-output >> pairs. You can get 100% accuracy on this pretty easily if you are careful >> with your bias-variance decomposition. This is a fun one for neural >> networks too! >> >> Essentially any machine learning book will delve into greater detail on >> this as the US postal digit data has been around for a long time. I think >> Kaggle even had this as a training exercise for a while, so there's >> probably a ton of discussion of various methods and algorithms on their >> message boards. >> >> For kicks why don't you compare k-means clustering to logistic regression >> using Mahout? > Hi Angus, Chameera's requirement is to classify handwritten digits, so > could you please explain how could K-means clustering be helpful in this > scenario? Of course it would find different clusters but this is still a > classification problem. Please correct me if I'm wrong. > > Thanks, > > >> >> -Angus >> >> >> >> >> On Thu, Jan 23, 2014 at 8:00 PM, Chameera Wijebandara < >> [email protected]> wrote: >> >>> Hi, >>> >>> I am trying to classify handwritten digits using mahout classification. >> Any >>> suggestion to come up with good solution? >>> >>> -- >>> Thanks, >>> Chameera > > > > -- > M.P. Tharindu Rusira Kumara > > Department of Computer Science and Engineering, > University of Moratuwa, > Sri Lanka. > +94757033733 > www.tharindu-rusira.blogspot.com
