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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/5953完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林軒田(Hsuan-Tien Lin) | |
| dc.contributor.author | Yu-Cheng Chou | en |
| dc.contributor.author | 周育正 | zh_TW |
| dc.date.accessioned | 2021-05-16T16:18:42Z | - |
| dc.date.available | 2016-08-20 | |
| dc.date.available | 2021-05-16T16:18:42Z | - |
| dc.date.copyright | 2013-08-20 | |
| dc.date.issued | 2013 | |
| dc.date.submitted | 2013-08-14 | |
| dc.identifier.citation | Peter Auer, Nicol`o Cesa-Bianchi, Paul Fischer, and Lehrstuhl Informatik. Finite-time
analysis of the multi-armed bandit problem. Machine Learning, (2-3):235–256, 2000. John Langford and Tong Zhang. The epoch-greedy algorithm for contextual multi-armed bandits. In Proceedings of the Conference on Neural Information Processing Systems, 2007. David D. Lewis and William A. Gale. A sequential algorithm for training text classifiers. In Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval, pages 3–12, 1994. Lihong Li, Wei Chu, John Langford, and Robert E. Schapire. A contextual-bandit approach to personalized news article recommendation. In Proceedings of the International Conference on World Wide Web, pages 661–670, 2010. Ming Li and Zhi-Hua Zhou. Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples. IEEE Transactions on Systems, Man, and Cybernetics, Part A, 37(6):1088–1098, 2007. R. M. Rangayyan, J. A. Fabio, and J. L. Desautels. A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs. Journal of the Franklin Institute, 344(3–4):312–348, 2007. Burr Settles. Active learning literature survey. Technical report, University ofWisconsin– Madison, 2009. S. Tong and D. Koller. Support vector machine active learning with applications to text classification. Journal of Machine Learning Research, 2:45–66, 2001. Esau Villatoro-Tello, Antonio Jua rez Gonza lez, Hugo Jair Escalante, Manuel Montes y Go mez, and Luis Villasen or Pineda. A two-step approach for effective detection of misbehaving users in chats. In Proceedings of the Conference and Labs of the Evaluation(Online Working Notes/Labs/Workshop), 2012. Thomas J. Walsh, Istvan Szita, Carlos Diuk, and Michael L. Littman. Exploring compact reinforcement-learning representations with linear regression. In Proceedings of the Conference on Uncertainty in Artificial Intelligence, pages 591–598, 2009. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/5953 | - |
| dc.description.abstract | 驗證問題是一個有很多應用且需要使用人力的問題。機器學習可以減少花費在驗證問題上的人力。透過結合驗證問題中的學習和驗證兩個階段，我們提出一個稱做``互動驗證'的新問題。這個新問題可以藉著自由分配學習和驗證來更有效的運用人力。我們提出使用情境式拉霸問題 (Contextual Bandit Problem) 中的上信賴界 (Upper Confidence Bound) 方法來解決互動驗證問題。在真實世界資料上的實驗結果證實了上信賴界可以有效的解決互動驗證問題 | zh_TW |
| dc.description.abstract | The verification problem comes with many applications and requires human efforts. Machine learning can help reduce human efforts spent on verification. By combining the learning and verification stages in a verification problem, we formalize the needs as a new problem called interactive verification. The problem allows an algorithm to flexibly use the limited human resource on learning and verification together. We propose to adopt upper confidence bound (UCB) algorithm, which has been widely used for the contextual bandit, to solve the interactive verification problem. Experiment results demonstrate that UCB has superior performance on interactive verification on many real-world datasets. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-16T16:18:42Z (GMT). No. of bitstreams: 1 ntu-102-R00922012-1.pdf: 1619705 bytes, checksum: efb0a29a60dde5a99f8f01e986cd087f (MD5) Previous issue date: 2013 | en |
| dc.description.tableofcontents | 致謝iii
中文摘要v Abstract vii 1 Introduction 1 2 Problem Setting 5 2.1 Interactive Verification Problem . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.3 Comparison to Active Learning Problem . . . . . . . . . . . . . . . . . . 8 2.4 Comparison to Contextual Bandit Problem . . . . . . . . . . . . . . . . . 9 3 Approaches 11 3.1 Greedy Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.2 Random then Greedy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.3 Uncertainty Sampling then Greedy . . . . . . . . . . . . . . . . . . . . . 13 3.4 Upper Confidence Bound . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.5 Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 4 Experiment 17 4.1 Dataset Generation and Experiment Setting . . . . . . . . . . . . . . . . 17 4.2 Effect of . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.3 Comparison of All Approaches . . . . . . . . . . . . . . . . . . . . . . . 20 4.4 Real-world Task . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 5 Conclusion 23 Bibliography 23 | |
| dc.language.iso | en | |
| dc.title | 以機器學習方法進行互動驗證 | zh_TW |
| dc.title | Machine Learning Approaches for Interactive Verification | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 101-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 林守德(Shou-De Lin),李育杰(Yuh-Jye Lee) | |
| dc.subject.keyword | 機器學習,主動學習,情境式拉霸問題, | zh_TW |
| dc.subject.keyword | Machine Learning,Active Learning,Contextual Bandit Problem, | en |
| dc.relation.page | 22 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2013-08-14 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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