Bridged Refinement for Transfer Learning
Bridged Refinement for Transfer Learning
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DOI：
10.1007/978-3-540-74976-9_31
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发表时间：
2007-09
期刊：
影响因子：
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通讯作者：
Dikan Xing;Wenyuan Dai;Gui-Rong Xue;Yong Yu
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文献类型：
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作者：
Dikan Xing;Wenyuan Dai;Gui-Rong Xue;Yong Yu
在传统的机器学习中，通常有一个假设，即训练和测试数据由相同的分布控制。当训练和测试数据来自不同的时间段或域时，可能会违反这一假设。在这种情况下，传统的机器学习方法不知道分布的变化可能会失败。本文提出了一种新的算法，即桥接细化，考虑到移位。该算法将无移位分类器预测的标签向目标分布进行修正，并以训练数据和测试数据的混合分布为桥梁，更好地实现从训练数据到测试数据的转换。在实验中，我们的算法成功地改进了三种最先进的算法预测的分类标签：支持向量机，朴素贝叶斯分类器和直推支持向量机在11个数据集上。错误率的相对减少平均约为50%。
There is usually an assumption in traditional machine learning that the training and test data are governed by the same distribution. This assumption might be violated when the training and test data come from different time periods or domains. In such situations, traditional machine learning methods not aware of the shift of distribution may fail. This paper proposes a novel algorithm, namelybridged refinement, to take the shift into consideration. The algorithm corrects the labels predicted by a shift-unaware classifier towards a target distribution and takes the mixture distribution of the training and test data as a bridge to better transfer from the training data to the test data. In the experiments, our algorithm successfully refines the classification labels predicted by three state-of-the-art algorithms: the Support Vector Machine, the naïve Bayes classifier and the Transductive Support Vector Machine on eleven data sets. The relative reduction of error rates is about 50% in average.