Friedman and koller friedman and koller
142 Algorithm:Learning the Structure of Bayesian Network Classifiers
To conclude, experience shows that the use of independence-based methods in semi-supervised learning is not promising.
Classification Driven Stochastic Structure Search 143
5. Classification Driven Stochastic Structure Search
Definition 7.1 The inverse error measure for structure S′is
1
where the summation is over the space of possible structures and
pS( |
|---|
We use Metropolis-Hastings sampling [Metropolis et al., 1953] to generate samples from the inverse error measure, without having to ever compute it for all possible structures. For constructing the Metropolis-Hastings sampling, we define a neighborhood of a structure as the set of directed acyclic graphs to which we can transit in the next step. Transition is done using a predefined set of possible changes to the structure; at each transition a change consists of a
