some problems in the multiboost training

We modified the feature extractor, because we didn't choose samples from training segment randomly before. And then the performance dropped. We thought it might be overfitting in short segments, for the cause that short samples could be chosen with more probability.

Now, I am trying to figure out this toolkit is implemented with what kind of theory. Because multi-class boosting has many variances, maybe we cannot define classes like what we have done.

here are some papers related with multiboost toolkit:
1. Aggregate Features and AdaBoost for Music Classification
http://www.iro.umontreal.ca/~casagran/docs/2006_ml_draft.pdf
"Classification with AdaBoost" part
2. a brief introduction to boosting
http://0rz.tw/b62Dn