Patent ID: 6529891
Filing Date: 2003-03-04
Classification: G06K,G06N,Y10S

Abstract:
A computer implemented method for training a mixture of Bayesian networks (MBNs), each of said MBNs having a plurality of hypothesis-specific networks (HSBNs) comprising nodes for discrete hidden variables, said HSBNs corresponding to respective states of a common hidden variable, said HSBNs also comprising nodes for observed variables, said observed variables representing observed data from empirical observations, said HSBNs having casual links between at least a subset of the nodes, said method of training comprising:for each one of said HSBNs conducting a parameter search for a set of changes in said probability parameters which improves the goodness of said one HSBN in predicting said observed data, and modifying the probability parameters of said one HSBN accordingly; for each one of said HSBNs, computing a structure score of said one HSBN reflecting the goodness of said one HSBN in predicting said observed data by: computing from said observed data expected complete model sufficient statistics (ECMSS); computing from said ECMSS sufficient statistics for said one HSBN; computing said structure score from said sufficient statistics; conducting a structure search for a change in said causal links which improves said structure search score, and modifying the causal links of said one HSBN accordingly; and computing from said ECMSS an expected sample size for each state of a hidden variable and deleting those states having a sample size less than a predetermined threshold, wherein the number of states of the hidden variable corresponds to a number of clusters, each cluster corresponding to one of said HSBNs.