What about the PassiveAggressive algorithms..??
I tried searching but I found results like Passive Aggressive
human(malefemale) behaviour. Any guidance..?
On Tue, Nov 23, 2010 at 9:48 PM, Ted Dunning <ted.dunning@gmail.com> wrote:
> One of the key points of the algorithm is that it is easier to learn to
> order things than it is to learn a proper metric.
>
> THis is related to the same sort of concept in supervised classifier
> learning as opposed to learning a recommender. Much previous work started
> with the observation that learning a correct score would natural provide
> correct ranking and did not examine whether learning to rank directly might
> provide a more feasible learning algorithm.
>
> The "lots of heavy math" problem that you encountered is not going to go
> away. There is no way to make sense of machine learning with out the math.
> Intuitionist approaches do not take you very far down the road because
> without being able to do the math, you can't tell what is going wrong.
>
> On Tue, Nov 23, 2010 at 7:50 AM, gagan chhabra <gagan.13031990@gmail.com
> >wrote:
>
> > A couple of months ago i posted a link for the research paper I am
> working
> > on, which is
> > based on pairwise similarity learning which being an problem in machine
> > learning.
> >
> > I still dont get the exact meaning what this paper has.
> >
> > What I have understood so far is that the paper address to design of a
> > algorithm which helps in
> > scaling the pairwise similarity learning of images by using Passive
> > Aggressive algorithms.
> >
> > But the main problem Passive Aggressive algorithms. I Googled it but I
> got
> > some results which were again
> > some papers having some heavy mathematics.
> >
> > so please can anyone tell me about them both in brief, what actually i'll
> > have to do to implement the algorithm
> > in paper below
> >
> > link for the paper:
> >
> >
> http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en//pubs/archive/35311.pdf
> > also what are passive aggressive algorithms!!
> >
> >
> > 
> > gagan
> >
>

gagan
