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      Scalable Greedy Algorithms for Transfer Learning

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          Abstract

          In this paper we consider the binary transfer learning problem, focusing on how to select and combine sources from a large pool to yield a good performance on a target task. Constraining our scenario to real world, we do not assume the direct access to the source data, but rather we employ the source hypotheses trained from them. We propose an efficient algorithm that selects relevant source hypotheses and feature dimensions simultaneously, building on the literature on the best subset selection problem. Our algorithm achieves state-of-the-art results on three computer vision datasets, substantially outperforming both transfer learning and popular feature selection baselines in a small-sample setting. We also present a randomized variant that achieves the same results with a fraction of the computational cost. Also, we theoretically prove that, under reasonable assumptions on the source hypotheses, our algorithm can learn effectively from few examples.

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          Author and article information

          Journal
          2014-08-06
          2015-10-08
          Article
          1408.1292
          3ac639a4-6d58-4bdf-8044-d3e9101c410e

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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          Custom metadata
          cs.CV cs.LG

          Computer vision & Pattern recognition,Artificial intelligence
          Computer vision & Pattern recognition, Artificial intelligence

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