Untitled

Abstract. We present a collective approach to learning a Bayesian network from distributed heterogenous data. In this approach, we first learn a local Bayesian network at each site using the local data. Then each site identifies the observations that are most likely to be evidence of coupling between local and non-local variables and transmits a subset of these observations to a central site. Another Bayesian network is learnt at the central site using the data transmitted from the local site. The local and central Bayesian networks are combined to obtain a collective Bayesian network, that models the entire data. Experimental results and theoretical justification that demonstrate the feasibility of our approach are presented.

show abstract

Untitled

Cited by 29 publications

References 10 publications

On Chow-Liu Forest Based Regularization of Deep Belief Networks

On Chow-Liu Forest Based Regularization of Deep Belief Networks

Learning Bayesian Network Parameters from Small Data Set: A Spatially Maximum a Posteriori Method

Collective mining of Bayesian networks from distributed heterogeneous data

Contact Info

Product

Resources

About