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A Bayesian framework for active learning
2010
The 2010 International Joint Conference on Neural Networks (IJCNN)
We describe a Bayesian framework for active learning for non-separable data, which incorporates a query density to explicitly model how new data is to be sampled. The model makes no assumption of independence between queried data-points; rather it updates model parameters on the basis of both observations and how those observations were sampled. A 'hypothetical' look-ahead is employed to evaluate expected cost in the next time-step. We show the efficacy of this algorithm on the probabilistic
doi:10.1109/ijcnn.2010.5596917
dblp:conf/ijcnn/FredlundEF10
fatcat:tcks3u5e6rhydikexmvrsvp6wi