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vfbn2 File Reference

Detailed Description

Learns the structure of a BeliefNet from a very large data set using sampling and a new search proceedure.

Learns the structure and parameters of a Bayesian network, accelerated with sampling as described in this paper. All variables must be categorical. vfbn2 searches for high scoring Bayesian network structures by considering adding and removing every possible edge (but not reversing as traditional methods do), making the one that has highest score on training data, and repeating until no change improves the score. Unlike other learners, vfbn2 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 vfbn2 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). vfbn2 also differs from traditional Bayesian network learners by running the search for the model at each variable in parallel. Whenever the statistical tests at one node indicate that a change should be made it is made, and this fact is broadcast to the searches at the other nodes which immediately stop considering any changes that would add a cycle to the network given the change just made.

vfbn2 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


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