> The Spider Objects

>               ADABOOST

>        A=ADABOOST(C,H) returns a template object initialized with learning algorithm C and hyperparameters H.

>         The template object is a simple linear support vector machine, with
>          soft margin hyperparameter C. Please use this template to
>          implement your own algorithms.

>        Hyperparameters, and their defaults
>        kmax = 5                -- number of weak learners

>        Methods:
>         train, test

>        Model:
>         child                  -- learning algorithm
>        Example:

>          Use adaboost with 1-knn as weak learner and validate with 2 fold cross validation.
>        c1=[2,0];
>        c2=[-2,0];
>        X1=randn(50,2)+repmat(c1,50,1);
>        X2=randn(50,2)+repmat(c2,50,1);

>        d=data([X1;X2],[ones(50,1);-ones(50,1)]);
>        [r,a]=train(cv(adaboost(knn),'folds=2'),d);

> | Reference : The boosting approach to machine learning: An overview

> | Author : Robert E. Schapire

> | Link : http://www.cs.princeton.edu/~schapire/uncompress-papers.cgi/msri.ps