Sciweavers

Share
ICCV
2005
IEEE

Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering

10 years 1 months ago
Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering
In this paper, a new learning framework?probabilistic boosting-tree (PBT), is proposed for learning two-class and multi-class discriminative models. In the learning stage, the probabilistic boosting-tree automatically constructs a tree in which each node combines a number of weak classifiers (evidence, knowledge) into a strong classifier (a conditional posterior probability). It approaches the target posterior distribution by data augmentation (tree expansion) through a divide-and-conquer strategy. In the testing stage, the conditional probability is computed at each tree node based on the learned classifier, which guides the probability propagation in its sub-trees. The top node of the tree therefore outputs the overall posterior probability by integrating the probabilities gathered from its sub-trees. Also, clustering is naturally embedded in the learning phase and each sub-tree represents a cluster of certain level. The proposed framework is very general and it has interesting conn...
Zhuowen Tu
Added 15 Oct 2009
Updated 15 Oct 2009
Type Conference
Year 2005
Where ICCV
Authors Zhuowen Tu
Comments (0)
books