Method and System for Evaluation Using Probabilistic Boosting Trees
AssigneeSiemens Aktiengesellschaft
InventorsNeil Birkbeck, Michal Sofka, Shaohua Kevin Zhou
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Abstract
A method and system for evaluating probabilistic boosting trees is disclosed. In an embodiment, input data is received at a graphics processing unit. A weighted empirical distribution associated with each node of the probabilistic boosting tree is determined using a stack implementation. The weighted empirical distribution associated with each node is added to a total posterior distribution value.
Claims51
A method for training a probabilistic boosting tree, comprising:
- receiving training data at a graphics processing unit (GPU);
- dividing the training data into a first dataset and a second dataset using a classifier;
- training a first sub-tree and a second sub-tree at the GPU, the first sub-tree using the first dataset and the second sub-tree using the second dataset;
- generating a posterior distribution model based on the trained first sub-tree and the trained second sub-tree.
The method of claim 7, wherein the parallel computing architecture is Compute Unified Device Architecture (CUDA).
A method for determining the posterior distribution of a probabilistic boosting tree, comprising:
- receiving input data at a graphics processing unit (GPU)
- determining a weighted empirical distribution associated with each node of the probabilistic boosting tree using a stack implementation;
- adding the weighted empirical distribution associated with each node to a total posterior distribution value.
A method for evaluating a forest of probabilistic boosting trees, comprising:
- receiving input data at a graphics processing unit (GPU);
- evaluating the plurality of probabilistic boosting trees using a stack implementation;
- generating a combined posterior distribution based on a posterior distribution of each of the plurality of probabilistic boosting trees.
A system for training a probabilistic boosting tree, comprising:
- means for receiving training data at a graphics processing unit (GPU);
- means for dividing the training data into a first dataset and a second dataset using a classifier;
- means for training a first sub-tree and a second sub-tree at the GPU, the first sub-tree using the first dataset and the second sub-tree using the second dataset;
- means for generating a posterior distribution model based on the trained first sub-tree and the trained second sub-tree.
A system for determining the posterior distribution of a probabilistic boosting tree, comprising:
- means for receiving input data at a graphics processing unit (GPU)
- means for determining a weighted empirical distribution associated with each node of the probabilistic boosting tree using a stack implementation;
- means for adding the weighted empirical distribution associated with each node to a total posterior distribution value.
A system for evaluating a forest of probabilistic boosting trees, comprising:
- means for receiving input data at a graphics processing unit (GPU);
- means for evaluating the plurality of probabilistic boosting trees using a stack implementation;
- means for generating a combined posterior distribution based on a posterior distribution of each of the plurality of probabilistic boosting trees.
A non-transitory computer readable medium encoded with computer executable instructions for training a probabilistic boosting tree, the computer executable instructions defining steps comprising:
- receiving training data at a graphics processing unit (GPU);
- dividing the training data into a first dataset and a second dataset using a classifier;
- training a first sub-tree and a second sub-tree at the GPU, the first sub-tree using the first dataset and the second sub-tree using the second dataset;
- generating a posterior distribution model based on the trained first sub-tree and the trained second sub-tree.
A non-transitory computer readable medium encoded with computer executable instructions for determining the posterior distribution of a probabilistic boosting tree, the computer executable instructions defining steps comprising:
- receiving input data at a graphics processing unit (GPU);
- determining a weighted empirical distribution associated with each node of the probabilistic boosting tree using a stack implementation;
- adding the weighted empirical distribution associated with each node to a total posterior distribution value.
A non-transitory computer readable medium encoded with computer executable instructions for evaluating a forest of probabilistic boosting trees, the computer executable instructions defining steps comprising:
- receiving input data at a graphics processing unit (GPU);
- evaluating the plurality of probabilistic boosting trees using a stack implementation;
- generating a combined posterior distribution based on a posterior distribution of each of the plurality of probabilistic boosting trees.
Dependencies are inferred from the claim text.