[mlpack] GOSC 2014 idea of AdaBoost implementation(MingJun Liu)

[闫林]

I implemented Adaboost before
https://github.com/godspeed1989/CNS186-Viola-Jones
Hope it helpful


2014-03-01 4:34 GMT+08:00 Ryan Curtin <gth671b at mail.gatech.edu>:

> On Thu, Feb 27, 2014 at 09:56:09AM +0800, Mj Liu wrote:
> > Hi all,
> >     I'm from University of Science and Techonology of China, and working
> as
> > a intern student at Chinese Academy of Sciences now. My major is software
> > engineering. I would like to join in the GSOC project and apply for the
> > AdaBoosting part. Though I am new here, I am very interest in the project
> > of MLPACK.
> >     Udit Saxena provided several good suggestions about the weak learners
> > in the mail list for the AdaBoost project. I also have several questions
> > about how the AdaBoost can be integrated into the mlpack prject, and what
> > kind of APIs shall be provide to the users. And I think several problems
> > shall be talked settled before begin to work on the project:
> >     - What shall the archetecture(API) be? How can the users call the
> > method of AdaBoost? May the users can define their own learner method and
> > call like: AdaBoost(data,  function learner()), or by AdaBoost(data,
> > function inner-learner())?
>
> Hi MingJun,
>
> The API of the AdaBoost implementation is an open question, but it
> should definitely be as similar as possible to existing mlpack methods
> (found in src/mlpack/methods/).  I have spent some time thinking about
> the best way to do this.  mlpack does not use inheritance and I would
> prefer for things to remain that way.  Instead, template parameters are
> used (this is called policy-based design).  So for AdaBoost, I would
> think some API like this might be the way to go:
>
> template<
>   typename WeakClassifier1Type,
>   typename WeakClassifier2Type,
>   typename WeakClassifier3Type,
>   ...
> >
> class AdaBoost;
>
> Then AdaBoost::Classify() could be used to actually perform the boosted
> classification.  It would be assumed that each WeakClassifierXType class
> implemented a Classify() method of its own (and potentially a Train()
> method).
>
> This is preferable to function pointers because it is what's done in the
> rest of mlpack.
>
> >     - And how to control the steps of iteration, or the precision of
> error,
> > or initialization of  the user defined learners? May the users just set
> one
> > of MAX_ITERATION_STEP or PRECISION_ERROR , and the mlpack provide default
> > of both.
>
> I would imagine that these could just be parameters to the AdaBoost
> constructor, and could be configurable by the user.  See the other
> mlpack methods for an idea of what I mean.
>
> >     - As to the weak learners, I think the Linear Perceptron(LP),
> Multi-LP,
> > shall be included. Because users may need these to build their new
> > algorithms.
>
> Yes, this project should definitely include the implementation of a few
> weak learners, because mlpack does not really have any at the moment.
> These weak learners should be implemented as efficiently as possible,
> and we can verify this by using the benchmarking system (
> https://www.github.com/zoq/benchmarks ) to compare against other
> implementations.
>
> >     - Since there are so many variants of AdaBoost [
> > http://www.site.uottawa.ca/~stan/csi5387/boost-tut-ppr.pdf] [
> > http://www.site.uottawa.ca/~stan/csi5387/boost-tut-ppr.pdf] [
> > http://colt2008.cs.helsinki.fi/papers/26-Shwartz.pdf], experiments
> shall be
> > implemented to test which shall be kept in MLPACK project.
>
> Yes; better yet, the AdaBoost class should be templatized in such a way
> that the user can easily implement their own variant of AdaBoost.
>
> --
> Ryan Curtin    | "This room is green."
> ryan at ratml.org |   - Kazan
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