Active Learning with Cross Domain Transfer

• Main Authors: Xu, Jun-Yao, 許峻耀
• Other Authors: 葉倚任
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/ae94s8

碩士 === 國立高雄師範大學 === 數學系 === 107 === Machine learning technologies has attracted a lot of attention, it is becoming pop- ular and be widely applied in most recent years. In the real world applications, we can get a huge amount of data, but these data are unlabeled data. However, many classic classication algorithms we often used cannot be used directly. Since learning a good classier usually need large quantities of labels information, but get labels information is usually dicult or expensive (need time and money). Even if we just labeled some of the training data, the time and money cost of labeling data is unimaginable. Therefore, we used active learning, an algorithm that can reduces the training set and labeling cost as much as possible. And combine transfer learning to solve the weaknesses of active learning algorithms: initial selection. Moreover, we also try to improve the performance of active learning in the beginning. In this paper, we propose a simple active learning framework with cross domain transfer, which using labeled data from a dierent (but related) tasks to improve the perfor- mance of an active learner. We use some commonly used transfer learning data sets to conrm our method analysis. Moreover, the results of experiment verify the eectiveness of the method we proposed.