To reduce computation time for boosted models without substantially losing accuracy, the influence trimming technique can be employed. As the training algorithm proceeds and the number of trees in the ensemble is increased, a larger number of the training samples are classified correctly and with increasing confidence, thereby those samples receive smaller weights on the subsequent iterations. Examples with a very low relative weight have a small impact on the weak classifier training. Thus, such examples may be excluded during the weak classifier training without having much effect on the induced classifier. This process is controlled with the weight_trim_rate parameter. Only examples with the summary fraction weight_trim_rate of the total weight mass are used in the weak classifier training. Note that the weights for all training examples are recomputed at each training iteration. Examples deleted at a particular iteration may be used again for learning some of the weak classifiers further 

See alsocv::ml::Boost Prediction with Boost

StatModel::predict(samples, results, flags) should be used. Pass flags=StatModel::RAW_OUTPUT to get the raw sum from Boost classifier.

8、关于训练过程打印信息的解释

1）POS count : consumed   n1 : n2

每次都调用updateTrainingSet( requiredLeafFARate, tempLeafFARate );函数

bool CvCascadeClassifier::updateTrainingSet( double minimumAcceptanceRatio, double& acceptanceRatio) { int64 posConsumed = 0, negConsumed = 0; imgReader.restart(); int posCount = fillPassedSamples( 0, numPos, true, 0, posConsumed );//Consumed消耗 if( !posCount ) return false; cout << "POS count : consumed " << posCount << " : " << (int)posConsumed << endl;//这就是打印信息，我的理解是这个stage判成正样本数和正样本数 int proNumNeg = cvRound( ( ((double)numNeg) * ((double)posCount) ) / numPos ); // apply only a fraction of negative samples. double is required since overflow is possible int negCount = fillPassedSamples( posCount, proNumNeg, false, minimumAcceptanceRatio, negConsumed ); if ( !negCount ) return false; curNumSamples = posCount + negCount; acceptanceRatio = negConsumed == 0 ? 0 : ( (double)negCount/(double)(int64)negConsumed ); cout << "NEG count : acceptanceRatio " << negCount << " : " << acceptanceRatio << endl;//打印信息，我的理解是 return true; }int CvCascadeClassifier::fillPassedSamples( int first, int count, bool isPositive, double minimumAcceptanceRatio, int64& consumed ) { int getcount = 0; Mat img(cascadeParams.winSize, CV_8UC1); for( int i = first; i < first + count; i++ ) { for( ; ; ) { if( consumed != 0 && ((double)getcount+1)/(double)(int64)consumed <= minimumAcceptanceRatio ) return getcount; bool isGetImg = isPositive ? imgReader.getPos( img ) : imgReader.getNeg( img ); if( !isGetImg ) return getcount; consumed++; featureEvaluator->setImage( img, isPositive ? 1 : 0, i ); if( predict( i ) == 1.0F ) { getcount++; printf("%s current samples: %d\r", isPositive ? "POS":"NEG", getcount); break; } } } return getcount; }int CvCascadeClassifier::predict( int sampleIdx ) { CV_DbgAssert( sampleIdx < numPos + numNeg ); for (vector< Ptr<CvCascadeBoost> >::iterator it = stageClassifiers.begin(); it != stageClassifiers.end(); it++ ) { if ( (*it)->predict( sampleIdx ) == 0.f ) return 0; } return 1; }float CvCascadeBoost::predict( int sampleIdx, bool returnSum ) const { CV_Assert( weak ); double sum = 0; CvSeqReader reader; cvStartReadSeq( weak, &reader ); cvSetSeqReaderPos( &reader, 0 ); for( int i = 0; i < weak->total; i++ ) { CvBoostTree* wtree; CV_READ_SEQ_ELEM( wtree, reader ); sum += ((CvCascadeBoostTree*)wtree)->predict(sampleIdx)->value; } if( !returnSum ) sum = sum < threshold - CV_THRESHOLD_EPS ? 0.0 : 1.0; return (float)sum; }

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