Editing Boosting (machine learning) (section)

===Problem of object categorization===
[[Object categorization from image search|Object categorization]] is a typical task of [[computer vision]] that involves determining whether or not an image contains some specific category of object.  The idea is closely related with recognition, identification, and detection.  Appearance based object categorization typically contains [[feature extraction]], [[learning#3|learning]] a [[Classifier (mathematics)|classifier]], and applying the classifier to new examples.  There are many ways to represent a category of objects, e.g. from [[Shape analysis (digital geometry)|shape analysis]], [[bag of words model]]s, or local descriptors such as [[Scale-invariant feature transform|SIFT]], etc.  Examples of [[supervised learning|supervised classifiers]] are [[Naive Bayes classifier]]s, [[support vector machine]]s, [[mixtures of Gaussians]], and [[Artificial neural network|neural networks]].  However, research{{Which|date=October 2018}} has shown that object categories and their locations in images can be discovered in an [[Unsupervised learning|unsupervised manner]] as well.<ref>Sivic, Russell, Efros, Freeman & Zisserman,  "Discovering objects and their location in images", ICCV 2005</ref>