Editing Boosting (machine learning) (section)

===Boosting for binary categorization===
AdaBoost can be used for face detection as an example of [[binary categorization]]. The two categories are faces versus background. The general algorithm is as follows:
#Form a large set of simple features
#Initialize weights for training images
#For T rounds
##Normalize the weights
##For available features from the set, train a classifier using a single feature and evaluate the training error
##Choose the classifier with the lowest error
##Update the weights of the training images: increase if classified wrongly by this classifier, decrease if correctly
#Form the final strong classifier as the linear combination of the T classifiers (coefficient larger if training error is small)
After boosting, a classifier constructed from 200 features could yield a 95% detection rate under a <math>10^{-5}</math> [[Type I and type II errors|false positive rate]].<ref>P. Viola, M. Jones, "Robust Real-time Object Detection", 2001</ref>

Another application of boosting for binary categorization is a system that detects pedestrians using [[patterns]] of motion and appearance.<ref>{{cite conference|first1=P.|last1=Viola|first2=M.|last2=Jones|first3=D.|last3=Snow|title=Detecting Pedestrians Using Patterns of Motion and Appearance|conference=ICCV|year=2003|url=http://www.merl.com/publications/docs/TR2003-90.pdf |archive-url=https://ghostarchive.org/archive/20221009/http://www.merl.com/publications/docs/TR2003-90.pdf |archive-date=2022-10-09 |url-status=live}}</ref> This work is the first to combine both motion information and appearance information as features to detect a walking person. It takes a similar approach to the [[Viola–Jones object detection framework|Viola-Jones object detection framework]].