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Workshop on Transfer Learning for Structured Data
Recently, transfer learning (TL) has gained much popularity as an approach to reduce the training-data calibration effort as well as improve generalization performance of learning tasks. Unlike traditional learning, transfer learning methods make the best use of data from one or more source tasks in order to learn a target task. Many previous works on transfer learning have focused on transferring
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TOKUNAGA Hiroyuki on Twitter: "Large Scale Learning to Rank (D. Sculley, NIPS 2009) にすごく速い順位学習のアルゴリズムの話が出てた。実装も公開されている。 http://code.google.com/p/sofia-ml/"
NIPSに参加しました。 - Yasuo Tabeiの日記
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