Main Page | Modules | Data Structures | File List | Globals | Related Pages

vfbn1 File Reference

Detailed Description

Learns the structure of a BeliefNet from a very large data set using sampling.

Learns the structure and parameters of a Bayesian network, accelerated with sampling as described in this paper. All variables must be categorical. vfbn1, like other Bayesian network learning programs, searches for high scoring Bayesian network structures by considering adding, removing, and reversing every possible edge, making the one that has highest score on training data, and repeating until no change improves the score. Unlike other learners, vfbn1 uses statistical tests and only uses enough data to be sure that it knows which change is best with high confidence (see the -delta parameter below). This allows vfbn1 to be much faster than traditional methods when there is enough data to make good decisions. It also allows it to learn from data streams (see the -stdin flag below). VFML also includes the vfbn2 algorithm which changes the search procedure used so that it can be faster and more scalable.

vfbn1 takes input and does output in c4.5 format. It expects to find the files <stem>.names and <stem>.data.

Wish List:
An API to this learner like the one to learning BeliefNet structure in beliefnet-engine.h


Generated for VFML by doxygen hosted by Logo