Editing Boosting (machine learning) (section)

===Boosting for multi-class categorization===
Compared with binary categorization, [[multi-class categorization]] looks for common features that can be shared across the categories at the same time.  They turn to be more generic [[Edge detection|edge]] like features. During learning, the detectors for each category can be trained jointly. Compared with training separately, it [[Generalization|generalizes]] better, needs less training data, and requires fewer features to achieve the same performance.
 
The main flow of the algorithm is similar to the binary case. What is different is that a measure of the joint training error shall be defined in advance. During each iteration the algorithm chooses a classifier of a single feature (features that can be shared by more categories shall be encouraged). This can be done via converting [[multi-class classification]] into a binary one (a set of categories versus the rest),<ref>A. Torralba, K. P. Murphy, et al., "Sharing visual features for multiclass and multiview object detection", IEEE Transactions on PAMI 2006</ref> or by introducing a penalty error from the categories that do not have the feature of the classifier.<ref>A. Opelt, et al., "Incremental learning of object detectors using a visual shape alphabet", CVPR 2006</ref>

In the paper "Sharing visual features for multiclass and multiview object detection", A. Torralba et al. used [[GentleBoost]] for boosting and showed that when training data is limited, learning via sharing features does a much better job than no sharing, given same boosting rounds. Also, for a given performance level, the total number of features required (and therefore the run time cost of the classifier) for the feature sharing detectors, is observed to scale approximately [[logarithm]]ically with the number of class, i.e., slower than [[linear]] growth in the non-sharing case. Similar results are shown in the paper "Incremental learning of object detectors using a visual shape alphabet", yet the authors used [[AdaBoost]] for boosting.