OpenCV 2.4.3 Haar Classifier Error AdaBoost misclass

Question

I am using OpenCV 2.4.3 on Ubuntu 12.10 64bit and when I run opencv_training I get an error message shown below. The training continues so I don't think it is a critical error but nonetheless it blatantly says 'Error'. I can't seem to find any solutions for this - what does it mean ( what is AdaBoost ) , why is it complaining about a 'misclass' , and how can I fix it? Anything I found on Google referred to this as simply a 'warning' and basically to forget about it. Thanks!

cd dots ; nice -20 opencv_haartraining -data dots_haarcascade -vec samples.vec -bg negatives.dat -nstages 20 -nsplits 2 -minhitrate 0.999 -maxfalsealarm 0.5 -npos 13 -nneg 10 -w 10 -h 10 -nonsym -mem 4000 -mode ALL
Data dir name: dots_w10_h10_haarcascade
Vec file name: samples.vec
BG  file name: negatives.dat, is a vecfile: no
Num pos: 13
Num neg: 10
Num stages: 20
Num splits: 2 (tree as weak classifier)
Mem: 4000 MB
Symmetric: FALSE
Min hit rate: 0.999000
Max false alarm rate: 0.500000
Weight trimming: 0.950000
Equal weights: FALSE
Mode: ALL
Width: 10
Height: 10
Applied boosting algorithm: GAB
Error (valid only for Discrete and Real AdaBoost): misclass
Max number of splits in tree cascade: 0
Min number of positive samples per cluster: 500
Required leaf false alarm rate: 9.53674e-07
Stage 0 loaded
Stage 1 loaded
Stage 2 loaded
Stage 3 loaded
Stage 4 loaded
Stage 5 loaded
Stage 6 loaded
Stage 7 loaded

Tree Classifier
Stage
+---+---+---+---+---+---+---+---+
|  0|  1|  2|  3|  4|  5|  6|  7|
+---+---+---+---+---+---+---+---+

   0---1---2---3---4---5---6---7

Number of features used : 7544

Parent node: 7

*** 1 cluster ***
POS: 13 96 0.135417

Answer 1

I don't think this is an error message, rather it is a print out describing how the algorithm will measure it's internal error rate. In this case it is using misclassification of the examples. Real and discrete adaboost will map input samples onto the output range [0,1] so there is a meaningful way of measuring the inaccuracy of the algorithm. If a different variant of adaboost is being used, this error measure might cease to be meaningful.