Graph based sampling

Details Graph based sampling Graph based sampling

2007-08-16

2010-11-02

Sparks, Donald (Department: 2129)

Data processing: artificial intelligence

Machine learning

Reexamination Certificate

active

07827123

ABSTRACT:
An iterative method of sampling real world event data to generate a subset of data that is used for training a classifier. Graph Based Sampling uses an iterative process of evaluating and adding randomly selected event data sets to a training data set. In Graph Based Sampling, at each iteration, a two event data sets are randomly selected from a stored plurality of event data sets. A proximity function is used to generate a correlation or similarity value between each of these randomly selected real world event data sets, and the current training data set. One of the randomly selected event data sets is then added to the training data set based on the proximity value. This process of selection and addition is repeated until the subset of training set is a pre-determined size.

REFERENCES:
patent: 6584456 (2003-06-01), Dom et al.
patent: 7421114 (2008-09-01), Brandt

Affiliated with

Also associated with

Rating

Graph based sampling does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Graph based sampling, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Graph based sampling will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFUS-PAI-O-4244289