[Jboost-users] weights and weak learner

[Gungor P. <pol...@Pr...>]

Hi Everybody,

I am working on a modification of boosting on genomic data. I have some 
questions on Jboost,

1) First question is about the weight input. The meaning(higher weight 
implies greater importance to classify correctly) is fundamentally 
important for us since it is the heart of our research project. How does 
the algorithm do that? Is there any paper related to this idea? or is it 
just  a  practical emprical method just by changing the initial 
distribution? Do you guys know anything about that? Any information 
about this thing will help me very much. Also what is the bug currently 
in the weighting? looking for the news...
|weight| 	an initial weighting of the data (higher weight implies 
greater importance to classify correctly) THERE IS A BUG IN WEIGHTING IN 
MOST VERSIONS. MORE NEWS SOON. 	default = 1.0 	Optional


2) Second question is about the weak learner Jboost use. Since my data 
features are Real Values (not binary or discrete but -inf to +inf Real 
Numbers), I think I should use decision stumps with real thresholds. 
Does the algorithm consider such a thing (for binary feature a simpler 
stump should be used and for real valued another one)?

3)For the modification, are all the source codes in the SRC folder ?

Best,

Gungor