You can do supervised learning by outputing the clusters and labeling them 09.
> On Jan 23, 2014, at 10:34 PM, Tharindu Rusira <tharindurusira@gmail.com> wrote:
>
> On Fri, Jan 24, 2014 at 9:50 AM, Angus Macnab <angus.macnab@gmail.com>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 inputoutput
>> pairs. You can get 100% accuracy on this pretty easily if you are careful
>> with your biasvariance 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 kmeans clustering to logistic regression
>> using Mahout?
> Hi Angus, Chameera's requirement is to classify handwritten digits, so
> could you please explain how could Kmeans 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 <
>> chameerawijebandara@gmail.com> 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.tharindurusira.blogspot.com
