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中文摘要：
迁移学习是对传统监督学习的扩展，试图利用其他相关领域中的现存数据来帮助完成当前领域的学习任务．对于归纳式迁移学习算法，当目标领域只有少量数据时，已有的算法容易受到选择性偏差的影响，不能充分发挥相关领域数据的作用．为解决该问题，提出一种利用领域相似性的新途径：通过定义领域弱相似性的概念，将相似性的约束与目标分类器联系起来，能在训练过程中有效利用相关领域的大量数据，设计出一种基于支持向量机的迁移学习算法TrSVM，并给出求解过程．在大量数据集上的实验结果表明了新算法的有效性．
英文摘要：
Transfer learning algorithms focus on reusing related domain data to help solving learning tasks in the target domain. In this paper, we study the problem of inductive transfer learning. Most of the existing algorithms in inductive transfer learning might suffer from the problem of sample selection bias when the number of target domain data is too small. To address this problem, we propose to utilize domain similarity in a new approach. Through detailed discussion about the similarity of related domains, we define the concept of weak domain similarity. Using this concept to give additional constraints on the target classifiers, we develop a simple but effective approach to leverage the useful knowledge from the related domain, so that related domain data can be directly used in the training process. In this way, we are able to make the target classifier less sensitive to the small amount of target training data. Furthermore, we show that a modified SMO method can be applied to optimize the objective function in the algorithm effectively. The new algorithm is referred to as TrSVM, and can be seen as extension of support vector machines for transfer learning. Experiment results on extensive datasets show that TrSVM outperforms support vector machines and the state-of- the-art TrAdaBoost algorithm, and demonstrate the effectiveness of our algorithm.
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