  Learning with label proportions (LLP), which is a learning task that only
provides unlabeled data in bags and each bag's label proportion, has widespread
successful applications in practice. However, most of the existing LLP methods
don't consider the knowledge transfer for uncertain data. This paper presents a
transfer learning-based approach for the problem of learning with label
proportions(TL-LLP) to transfer knowledge from source task to target task where
both the source and target tasks contain uncertain data. Our approach first
formulates objective model for the uncertain data and deals with transfer
learning at the same time, and then proposes an iterative framework to build an
accurate classifier for the target task. Extensive experiments have shown that
the proposed TL-LLP method can obtain the better accuracies and is less
sensitive to noise compared with the existing LLP methods.
