cascade consists of many stages.
If first stage is already trained. Then the second stage training will use the rest of the samples which passed at first stage, or use all samples which are composed of the failing samples(at first stage) with increasing weight and passing samples(at first stage) with decreasing weight（like adaboost algorithm method）.
**Q1: Which cascade training method is, status 1 or 2 ?**
> *status 1:* using the rest of the samples which are pass at first stage.> *status 2:* using all samples which are composed of the failing samples(at first stage) with increasing weight and passing samples(at first stage) with decreasing weight.
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**Q2: What is the different of training cascade between two situation.**
> *situation 1:* set the detect rate and false positive rate at each stage.> *situation 2:* instead of setting the detect rate and false positive rate, control the number of feature at each stage. For example, use only one feature in each stage, and is it possible to get good detect rate ?
Please experts explain me about cascade training steps, thank you!
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