are an attractive modeling tool for human sensing, as they combine an intuitive
graphical representation with efficient algorithms for inference and learning.
Earlier work has demonstrated that boosted parameter learning could be used to
improve the performance of Bayesian network classifiers for complex multi-modal
inference problems such as speaker detection. In speaker detection, the goal is
to use video and audio cues to infer when a person is speaking to a user
interface. In this paper we introduce a new boosted structure learning algorithm
based on AdaBoost. Given labeled data, our algorithm modifies both the network
structure and parameters so as to improve classification accuracy. We compare
its performance to both standard structure learning and boosted parameter
learning on a fixed structure. We present results for speaker detection and for
the UCI ”chess” dataset.
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