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Author Chattopadhyay, Rita ♦ Fan, Wei ♦ Davidson, Ian ♦ Panchanathan, Sethuraman ♦ Ye, Jieping
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Batch-mode Active Learning ♦ Active Learning ♦ Joint Transfer ♦ Literature Perform Transfer ♦ Different Methodology ♦ Real Data ♦ Integrated Framework ♦ Small Set ♦ Superior Performance ♦ Unlabeled Target Domain Data ♦ Comprehensive Experiment ♦ Distribution Difference ♦ Active Learning Focus ♦ Much Interest ♦ Manual Annotation ♦ Re-weighted Source ♦ Active Learning Methodology ♦ Learning Address ♦ Separate Stage ♦ Common Problem ♦ Informative Sample ♦ Single Convex Optimization Problem ♦ Target Domain Data ♦ Queried Target Domain Data ♦ Common Objective ♦ Data Source ♦ Source Domain Data ♦ Insufficient Label
Abstract Active learning and transfer learning are two different methodologies that address the common problem of insufficient labels. Transfer learning addresses this problem by using the knowledge gained from a related and already labeled data source, whereas active learning focuses on selecting a small set of informative samples for manual annotation. Recently, there has been much interest in developing frameworks that combine both transfer and active learning methodologies. A few such frameworks reported in literature perform transfer and active learning in two separate stages. In this work, we present an integrated framework that performs transfer and active learning simultaneously by solving a single convex optimization problem. The proposed framework computes the weights of source domain data and selects the samples from the target domain data simultaneously, by minimizing a common objective of reducing distribution difference between the data set consisting of re-weighted source and the queried target domain data and the set of unlabeled target domain data. Comprehensive experiments on real data demonstrate the superior performance of the proposed approach. Proceedings of the 30 th
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study