Talk:AdaBoost

message: I'm not sure with the way the weight α is computed. In Viola and Jones paper, they  define α as 0.5*log(1-ε/ε) and not ln. If this is ln, α is negative, which is not possible.
:First, log is not necessarily logarithm with base 10, often it is the same as ln. Second, if it is, then log10(x) = ln(x)/ln(10), so log10(x) and ln(x) are either both positive or both negative. Since it is postulated that ε<0.5, then α is always positive. Finally, (1-ε)/ε ≠ 1-ε/ε. Victordk13 (talk) 10:19, 7 July 2008 (UTC)
replies: text: Good question. Originally, "weak classifier" was placed in a link that took one to the boosting page. But the term used on that page was "weak learner," and it was not immediately obvious that the meaning was similar. I added a few sentences here to make things clearer. -AlanUS (talk) 18:24, 19 November 2011 (UTC)

text: You're right. I fixed the page to use |error rate - 0.5|, though the criterion is that it be smaller than some value beta rather than exactly 0. -AlanUS (talk) 19:00, 19 November 2011 (UTC)

text: Shouldn't it be the other way around? Since all D's sum up to 1, ε can be between 0 and 1. If it is 0, then h_t classified all samples correctly, and | 0.5 - ε | would become 0.5. If ε is 1, then h_t classified all samples wrong, which is good, because we can add the inverse of h_t to our final classifier. In this case, | 0.5 - ε | would also yield 0.5. So, the highest value for | 0.5 - ε | is 0.5 and the lowest is obviously 0. We want to stop the algorithm, if it finds a good classifier, thus | 0.5 - ε | close to 0.5. Therefore the termination criterion should be | 0.5 - ε | >= beta.  — Preceding unsigned comment added by 137.193.76.44 (talk) 12:03, 11 February 2013 (UTC)

text: Shouldn't then updating D reflect this as well? currently it will reduce the weight of wrongly classified samples if ε>0.5.  — Preceding unsigned comment added by 109.226.48.167 (talk) 22:21, 26 June 2013 (UTC)
thread_title: Untitled

message: h_{t} = \underset{h \in \mathcal{H}}{\operatorname{argmin}} \; \epsilon_{t}, where  \epsilon_{t} = \sum_{i=1}^{m} D_{t}(i)[y_i \ne h(x_{i})] Wouldn't this make it more clear? (see french version of this article) --Madvermis (talk) 18:13, 8 August 2011 (UTC)
thread_title: Minimisation

message: http://www.inf.fu-berlin.de/inst/ag-ki/adaboost4.pdf (AdaBoost and the Super Bowl of Classifiers - A Tutorial on AdaBoost.)  — Preceding unsigned comment added by 89.134.90.209 (talk) 08:49, 8 October 2012 (UTC)
thread_title: Possibly broken link under "Implementations"

message: The introduction mentions that "as long as the performance of each one is slightly better than random guessing (i.e., their error rate is smaller than 0.5 for binary classification), the final model can be proven to converge to a strong learner." However, the model can converge even if the performance of a weak learner is worse than random guessing. In a case where a weak learner is worse than random alpha becomes negative and the weights for the subsequent learner are updated in the opposite direction. Hence, the intro should state that the final model can converge as long as the performance of a weak learner is not random (i.e. their error rater is smaller or larger than 0.5 for binary classification).  — Preceding unsigned comment added by Markthat (talk • contribs) 15:20, 4 May 2015 (UTC)
thread_title:  Introduction