Bayesian Multiple Instance Learning: Automatic Feature Selection and Inductive Transfer
published: Aug. 7, 2008, recorded: July 2008, views: 5506
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We propose a novel Bayesian multiple instance learning algorithm. This algorithm automatically identifies the relevant feature subset, and utilizes inductive transfer when learning multiple (conceptually related) classifiers. Experimental results indicate that the proposed baseline MIL method is more accurate than previous MIL algorithms and selects a much smaller set of useful features. Inductive transfer further improves the accuracy of the classifier as compared to learning each task individually.
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