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Sowmya Ramachandran
Bayesian networks provide a mathematically sound formalism for representing and reasoning with uncertain knowledge and are as such widely used. However, acquiring and capturing knowledge in this framework is difficult. There is a growing interest in formulating techniques for learning Bayesian networks inductively. While the problem of learning a Bayesian network, given complete data, has been explored in some depth, the problem of learning networks with unobserved causes is still open. In this proposal, we view this problem from the perspective of theory revision and present a novel approach which adapts techniques developed for revising theories in symbolic and connectionist representations. Thus, we assume that the learner is given an initial approximate network (usually obtained from a expert). Our technique inductively revises the network to fit the data better. Our proposed system has two components: one component revises the parameters of a Bayesian network of known structure, and the other component revises the structure of the network. The component for parameter revision maps the given Bayesian network into a multi-layer feedforward neural network, with the parameters mapped to weights in the neural network, and uses standard backpropagation techniques to learn the weights. The structure revision component uses qualitative analysis to suggest revisions to the network when it fails to predict the data accurately. The first component has been implemented and we will present results from experiments on real world classification problems which show our technique to be effective. We will also discuss our proposed structure revision algorithm, our plans for experiments to evaluate the system, as well as some extensions to the system.
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Citation:
Unpublished Ph.D. Thesis Proposal.
Bibtex:
@Misc{ramachandran:ftp95, title={Refinement of Bayesian Networks by Combining Connectionist and Symbolic Techniques}, author={Sowmya Ramachandran}, key={ramachandran proposal}, note={Unpublished Ph.D. Thesis Proposal}, url="http://www.cs.utexas.edu/users/ai-lab/?ramachandran:ftp95", year={1995} }
People
Sowmya Ramachandran
Ph.D. Alumni
sowmya [at] shai com
Areas of Interest
Machine Learning
Neural-Symbolic Learning
Theory and Knowledge Refinement
Uncertain and Probabilistic Reasoning
Labs
Machine Learning