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Heterogeneous Transfer Learning (HTL) algorithms leverage knowledge from a heterogeneous source domain to perform a task in a target domain. We present a novel HTL algorithm that works even where there are no shared features, instance correspondences and further, the two domains do not have identical labels. We utilize the label relationships via web-distance to align the data of the domains in the projected space, while preserving the structure of the original data
© Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rig...
Abstract. Transfer learning as a new machine learning paradigm has gained in-creasing attention late...
In this paper, a unified approach is presented to transfer learning that addresses several source an...
In many real-world problems, it is often time-consuming and expensive to collect labeled data. To al...
© 1979-2012 IEEE. The goal of transfer learning is to improve the performance of target learning tas...
The goal of transfer learning is to improve the performance of target learning task by leveraging in...
Transfer learning is a new machine learning and data mining framework that allows the training and t...
© 2012 IEEE. Distance metric learning plays a crucial role in diverse machine learning algorithms an...
Instance-correspondence (IC) data are potent resources for heterogeneous transfer learning (HeTL) du...
Most previous heterogeneous transfer learning methods learn a cross-domain feature mapping between h...
Distance metric learning (DML) is critical for a wide variety of machine learning algorithms and pat...
Distance metric learning plays a crucial role in diverse machine learning algorithms and application...
Most previous heterogeneous transfer learning methods learn a cross-domain feature mapping between h...
When a task of a certain domain doesn't have enough labels and good features, traditional supe...
Abstract Transfer learning has been demonstrated to be effective for many real-world applications as...
© Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rig...
Abstract. Transfer learning as a new machine learning paradigm has gained in-creasing attention late...
In this paper, a unified approach is presented to transfer learning that addresses several source an...
In many real-world problems, it is often time-consuming and expensive to collect labeled data. To al...
© 1979-2012 IEEE. The goal of transfer learning is to improve the performance of target learning tas...
The goal of transfer learning is to improve the performance of target learning task by leveraging in...
Transfer learning is a new machine learning and data mining framework that allows the training and t...
© 2012 IEEE. Distance metric learning plays a crucial role in diverse machine learning algorithms an...
Instance-correspondence (IC) data are potent resources for heterogeneous transfer learning (HeTL) du...
Most previous heterogeneous transfer learning methods learn a cross-domain feature mapping between h...
Distance metric learning (DML) is critical for a wide variety of machine learning algorithms and pat...
Distance metric learning plays a crucial role in diverse machine learning algorithms and application...
Most previous heterogeneous transfer learning methods learn a cross-domain feature mapping between h...
When a task of a certain domain doesn't have enough labels and good features, traditional supe...
Abstract Transfer learning has been demonstrated to be effective for many real-world applications as...
© Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rig...
Abstract. Transfer learning as a new machine learning paradigm has gained in-creasing attention late...
In this paper, a unified approach is presented to transfer learning that addresses several source an...
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