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boosting won't make miracles with learners performing worse than chance !

therefore, you should have a learner performing better than chance (err < 1/2)

now a "weak" learner is a learner which performs barely better than chance (err = 1/2-eps with eps small) ; it is the kind of learner which benefits most from boosting

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thank you for your response,

another point with adaboost,is in the algorith "-.thit is not clear 

for my case i'm using knn,so i train with knn,then i compute the error if

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basic boosting does not work well with knn because knn is a low variance model

there are however many variants of boosted knn and i do not know which you are experimenting

the two papers below give a good overview of this topic ; you might find the answer to some of your questions there :

http://www.stat.yale.edu/~lc436/papers/KNN/Garcia-Pedrajas_2009.pdf

https://pdfs.semanticscholar.org/97aa/0d3adec3981afa894bde8132d63fe1eb15b3.pdf

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Thank you Fabrice.

In my case i have a database with 192 features and labels in {-1,1}.For the number of instanace i can create the number i want.

I'm looking for a ML algorithm witch allow me to have a high accuracy in test higherthan a classic function which give me ~75 of good classification.

I tried knn alone with different k but i didn't have more.

SVM linear is not suitable here because the hig number of features.

I read about adaboost,i want to try with knn.

In conclousion i'm looking for a ML algorithm with give me better a classic function.

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hard to give advice without a hint of the problem ...

anyway,why not give a try to decision stumps (or short decision trees) as weak learners ?

for stumps, you can experiment with R

https://www.r-bloggers.com/improving-adaboosting-with-decision-stumps-in-r/

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thank you very much,