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中文摘要：
最近的年在转移学习见证增加的兴趣。这篇论文处理有从来源域的不同分布的目标域完全是未标记的分类问题，并且试图为未看见的数据造一个引入的模型。第一，我们在 transductive 转移学习的以前的工作分析班比率飘移的问题，并且建议使用一个正规化方法向需要的班比率移动。而且，我们为引入的转移学习开发一个混合规则化框架。它考虑三个因素，包括目标域由的分发几何学歧管规则化，由熵规则化的预言概率的熵价值，和由期望规则化优先的班。这个框架被用来使从来源域学习到目标域的引入的模特儿适应。最后，真实世界的文本数据的实验显示出我们转移学习的归纳方法的有效性。同时，它能处理未看见的测试点。增补材料为在 10.1007/s11434-009-0171-x 的这篇文章是可得到的并且为授权的用户是可存取的。
英文摘要：
Recent years have witnessed an increasing interest in transfer learning. This paper deals with the classification problem that the target-domain with a different distribution from the source-domain is totally unlabeled, and aims to build an inductive model for unseen data. Firstly, we analyze the problem of class ratio drift in the previous work of transductive transfer learning, and propose to use a normalization method to move towards the desired class ratio. Furthermore, we develop a hybrid regularization framework for inductive transfer learning. It considers three factors, including the distribution geometry of the target-domain by manifold regularization, the entropy value of prediction probability by entropy regularization, and the class prior by expectation regularization. This framework is used to adapt the inductive model learnt from the source-domain to the target-domain. Finally, the experiments on the real-world text data show the effectiveness of our inductive method of transfer learning. Meanwhile, it can handle unseen test points.
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