Talk:Boosting (machine learning)
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Bias vs. Variance
The first sentence of the article defines boosting as a method for reducing bias. Isn't this incorrect? If boosting provides generalization, and variance refers to the variance of the model for different training sets (i.e. high variance means overfitting), then boosting should reduce variance and thereby increase bias. I'm confused about this, could someone please comment?
--EmanueleLM (talk) 07:43, 1 June 2016 (UTC) No that's basicallt right since the wrt number of weak learners you can end up with bias (too few of them) or overfitting (too many of them). That's the best paper you can read about Boosting: http://rob.schapire.net/papers/explaining-adaboost.pdf
Anyway boosting almost always reduces bias and in practice unless you use a lot of learners does not increase variance significantly.
Strong vs. Weak
The explanation of strong vs. week learner is a bit confusing. Unfortunately, I am not the right person to explain it better. —Preceding unsigned comment added by 220.127.116.11 (talk) 08:42, 7 December 2010 (UTC)
Boosting is also a method for increasing the yield of a fisson bomb (Boosted fission weapon). Is that something that should be linked from this article? Or maybe put on the disambig. page for boost? --18.104.22.168 12:53, 1 June 2006 (UTC)
It should be in the disambug. page. Grokmenow 16:27, 10 July 2007 (UTC)
Oh, didnt see the date. Sorry about that. Grokmenow 16:27, 10 July 2007 (UTC)
Computer vision category
I removed this article from the computer vision category. Boosting is probably used by some people to solve CV problems but
- It is not a methodology developed within CV or specific to CV
- Boosting is already listed under the ensemble learning category which is linked to the CV category via maching learning.
--KYN 22:36, 27 July 2007 (UTC)
I removed two articles from the references section. Perhaps another references section should be started to include some of the additional research on boosting.
"branching program based boosters"
The paper cited in reference to "convex potential boosters [not being able to] withstand random classification noise" states that "branching program based boosters" can withstand noise.
It would be really swell if someone knowledgeable could explain what "branching program based boosters" are. (Sorry that I can't) —Preceding unsigned comment added by 22.214.171.124 (talk) 14:14, 23 March 2011 (UTC)
I think that this article : http://en.wikipedia.org/wiki/Boosting_methods_for_object_categorization
The content of Boosting methods for object categorization was merged into Boosting (machine learning) on 26th July 2016. For the contribution history and old versions of the redirected page, please see ; for the discussion at that location, see its talk page.