This paper presents Banner, a technique for using data to revise a given Bayesian network with noisy-or and noisy-and nodes, to improve its classification ...
Research in theory refinement has shown that biasing a learner with initial, approximately correct knowledge produces more accurate results than learning ...
Theory refinement is the task of updating a domain theory in the light of new cases, to be done automatically or with some expert as sistance.
Efficient Approximations for the MarginalLikelihood of Bayesian Networks with Hidden Variables. Special issue on learning with probabilistic representations.
Revising the structure of Bayesian network classifiers in the presence of missing data · Computer Science, Mathematics. Inf. Sci. · 2018.
Banner is presented, a technique for using data to revise a given Bayesian network with noisy-or and noisy-and nodes, to improve its classi cation accuracy ...
Theory refinement of bayesian networks with hidden variables · Structural learning of mixed noisy-OR Bayesian networks · Bayesian learning of Bayesian networks ...
In this paper, we derive a method to refine a Bayes network diagnostic model by exploiting constraints implied by expert decisions on test ordering.
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Dec 15, 2024 · Theory refinement is the task of updating a domain theory in the light of new cases, to be done automatically or with some expert assistance ...
This method can learn networks with hidden variables and discover hidden variables when necessary. Compared with previous techniques for learning Bayesian ...