(The third dimension, 1, is the bias / intercept term. You will
probably see this in the literature -- go have a look at a basic intro
to logistic regression. I found Andrew Ng's videos on Coursera a good
intro-level survey of exactly this.)
On Thu, Jun 28, 2012 at 3:57 PM, Ted Dunning wrote:
> On Thu, Jun 28, 2012 at 9:59 AM, damodar shetyo wrote:
>
>> This post is continuation to another mailing thread thats going on,Sorry
>> for creating a new thread but i was not getting mails from group before .
>>
>> Following code was implemented By Ted Dunning .Now i have few questions:
>>
>> 1)The point (x,y) has 2 dimensions.But why are we using 3 instead of 2
>> while creating DenseVector?
>> Vector v = new DenseVector(3); / / why 3 , why not 2?
>>
>> 2) In getVector method why we set v.set(2, 1); ??
>>
>> 3)Whats the use of setting lambda?
>>
>
> http://cseweb.ucsd.edu/~saul/teaching/cse291s07/L1norm.pdf
>
> (in this next, C is used instead of lambda)
> http://www.ttic.edu/sigml/symposium2011/papers/Moore+DeNero_Regularization.pdf
>
> (and in this one, alpha is used)
> http://en.wikipedia.org/wiki/Least_squares#LASSO_method
>
> 4)What happens if i increase or decrease learning rate?
>>
>
> It affects speed to converge. Very high starting point can be useful in
> some cases, but mostly just makes it take longer to converge. Very low
> starting point can make convergence fail.
>
> http://leon.bottou.org/projects/sgd