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
