Resources Contact Us Home
Probabilistic boosting tree framework for learning discriminative models

Image Number 5 for United States Patent #7702596.

A probabilistic boosting tree framework for computing two-class and multi-class discriminative models is disclosed. In the learning stage, the probabilistic boosting tree (PBT) automatically constructs a tree in which each node combines a number of weak classifiers (e.g., evidence, knowledge) into a strong classifier or conditional posterior probability. The PBT approaches the target posterior distribution by data augmentation (e.g., 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. In the training stage, a tree is recursively constructed in which each tree node is a strong classifier. The input training set is divided into two new sets, left and right ones, according to the learned classifier. Each set is then used to train the left and right sub-trees recursively.

  Recently Added Patents
Flat panel crystal display employing simultaneous charging of main and subsidiary pixel electrodes
Methods and apparatus for map detection with reduced complexity
Trash receptacle
Aggregating completion messages in a sideband interface
Image forming apparatus, information processing method, and storage medium for generating screen information
Build process management system
Multi display device and method of controlling the same
  Randomly Featured Patents
Method and apparatus for detecting biological activity
Generic information element
Heat pump system with selective space cooling
Sonically welded handle
Plastic clip
Control for water heater system
Tire cutting tool
Process for the preparation of nicotinic acid
Method for making a tube of a telescopic device