New approaches for heterogeneous transfer learning
In many real-world problems, it is often time-consuming and expensive to collect labeled data. To alleviate this challenge, transfer learning (TL) techniques that adapt a model from a related task with ample labeled data to a task of interest with little or no additional human supervision have been...
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| Format: | Thesis |
| Language: | English |
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2015
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| Online Access: | https://hdl.handle.net/10356/65532 |