Active Learning with Cross Domain Transfer
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 classica...
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ndltd-TW-107NKNU04790122019-10-05T03:47:09Z http://ndltd.ncl.edu.tw/handle/ae94s8 Active Learning with Cross Domain Transfer 應用跨領域遷移學習於主動學習 Xu, Jun-Yao 許峻耀 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. 葉倚任 2019 學位論文 ; thesis 23 en_US |
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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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author2 |
葉倚任 |
author_facet |
葉倚任 Xu, Jun-Yao 許峻耀 |
author |
Xu, Jun-Yao 許峻耀 |
spellingShingle |
Xu, Jun-Yao 許峻耀 Active Learning with Cross Domain Transfer |
author_sort |
Xu, Jun-Yao |
title |
Active Learning with Cross Domain Transfer |
title_short |
Active Learning with Cross Domain Transfer |
title_full |
Active Learning with Cross Domain Transfer |
title_fullStr |
Active Learning with Cross Domain Transfer |
title_full_unstemmed |
Active Learning with Cross Domain Transfer |
title_sort |
active learning with cross domain transfer |
publishDate |
2019 |
url |
http://ndltd.ncl.edu.tw/handle/ae94s8 |
work_keys_str_mv |
AT xujunyao activelearningwithcrossdomaintransfer AT xǔjùnyào activelearningwithcrossdomaintransfer AT xujunyao yīngyòngkuàlǐngyùqiānyíxuéxíyúzhǔdòngxuéxí AT xǔjùnyào yīngyòngkuàlǐngyùqiānyíxuéxíyúzhǔdòngxuéxí |
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