[mlpack] GSOC 2014: Introduction

[Udit Saxena]

Hey,

I came across the perceptron when I was looking further.

IEEE-Neural-weak
class<https://www.google.co.in/url?sa=t&rct=j&q=&esrc=s&source=web&cd=7&ved=0CGUQFjAG&url=http%3A%2F%2Fusers.ece.gatech.edu%2F~jic%2Fieee-neural-weak-class.pdf&ei=JpUPU9O6O4b8rAenh4CIDw&usg=AFQjCNHYdb2n_zYmNH3x1z9yIEsRHrtX8g&sig2=xjFw-bRoX4D6YzjIhAB3jQ&bvm=bv.61965928,d.bmk&cad=rja>.pdf
- the section on weak classifiers B.III. talks about perceptron as an
option, and also talks about combination of weak learners.

Maybe this gives you other ideas ?

And extending through templates might just end up being too simple, as at
least two of these are significantly different. But shouldn't be too much
of a problem, considering the spine remains the same. I was just wondering
which ones we would be interested in, but it seems as MingJun Liu says, we
can test through experimental implementations.

I will get in touch with Marcus Edel; just trying to get a simple patch put
in. What vcs do you use for the src? Could you help me with this ?

Debian and Ubuntu packaging: Oh ! You've been working on it ? Great. I did
want to club it this time but we'll see. Maybe, I would want to help
maintain it, along with keeping the Arch Linux's package up to date too.
Currently Arch's is outdated.
I had to build one from your src. Arch's one is 1.0.5 or 1.0.2. One of
them. Oh, and I'm on Arch.

I think a good way to implement them would be : ( a basic high level
overview )


   - implement a batch of weak learners. (say, 4). Will have to write
   separate functions/classes for this, for each weak learner.
   - write the adaboost class
   - and (through templates maybe?) allow the user to load separate
   instances of the weak learners as a potential input to the adaboost algo.

Thanks.

Udit.
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