| Summary: | 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.
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