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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林軒田(Lin-Hsuan Tien) | |
| dc.contributor.author | Yang-Han Jay | en |
| dc.contributor.author | 楊涵傑 | zh_TW |
| dc.date.accessioned | 2021-06-16T05:43:51Z | - |
| dc.date.available | 2016-01-01 | |
| dc.date.copyright | 2014-08-17 | |
| dc.date.issued | 2014 | |
| dc.date.submitted | 2014-08-11 | |
| dc.identifier.citation | [1] Tie-Yan Liu. Learning to rank for information retrieval. Foundations and Trends in Information Retrieval, 3(3):225–331, 2009.
[2] Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, and Hang Li. Learning to rank: from pairwise approach to listwise approach. In Proceedings of the 24th international conference on Machine learning, pages 129–136. ACM, 2007. [3] Cynthia Dwork, Ravi Kumar, Moni Naor, and D. Sivakumar. Rank aggregation methods for the web. In Proceedings of the 10th international conference on World Wide Web, WWW ’01, pages 613–622, New York, NY, USA, 2001. ACM. [4] Koby Crammer and Yoram Singer. Pranking with ranking. In Advances in Neural Information Processing Systems 14, pages 641–647. MIT Press, 2001. [5] Amnon Shashua and Anat Levin. Abstract. [6] Yoav Freund, Raj Iyer, Robert E Schapire, and Yoram Singer. An efficient boosting algorithm for combining preferences. The Journal of machine learning research, 4:933–969, 2003. [7] Thorsten Joachims. Optimizing search engines using clickthrough data. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 133–142. ACM, 2002. [8] Thorsten Joachims. Training linear svms in linear time. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 217–226. ACM, 2006. [9] Chris Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Greg Hullender. Learning to rank using gradient descent. In Proceedings of the 22nd international conference on Machine learning, pages 89–96. ACM, 2005. [10] Yisong Yue, Thomas Finley, Filip Radlinski, and Thorsten Joachims. A support vector method for optimizing average precision. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’07, pages 271–278, New York, NY, USA, 2007. ACM. [11] Jun Xu and Hang Li. Adarank: a boosting algorithm for information retrieval. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pages 391–398. ACM, 2007. [12] Qiang Wu, Chris JC Burges, Krysta M Svore, and Jianfeng Gao. Ranking, boosting, and model adaptation. Tecnical Report, MSR-TR-2008-109, 2008. [13] William W Cohen, Robert E Schapire, and Yoram Singer. Learning to order things. arXiv preprint arXiv:1105.5464, 2011. [14] Maria-Florina Balcan, Nikhil Bansal, Alina Beygelzimer, Don Coppersmith, John Langford, and Gregory B Sorkin. Robust reductions from ranking to classification. [15] Nir Ailon and Mehryar Mohri. Preference-based learning to rank. Machine Learning, 80(2-3):189–211, 2010. [16] Nir Ailon, Moses Charikar, and Alantha Newman. Aggregating inconsistent infor- mation: Ranking and clustering. J. ACM, 55(5):23:1–23:27, November 2008. [17] Claire Kenyon-Mathieu and Warren Schudy. How to rank with few errors. In Proceedings of the thirty-ninth annual ACM symposium on Theory of computing, pages 95–103. ACM, 2007. [18] Nir Ailon. Active learning ranking from pairwise preferences with almost optimal query complexity. In NIPS, pages 810–818, 2011. [19] Sahand Negahban, Sewoong Oh, and Devavrat Shah. Iterative ranking from pair-wise comparisons. In NIPS, pages 2483–2491, 2012. [20] Johannes Furnkranz and Eyke Hullermeier. Pairwise preference learning and ranking. In Machine Learning: ECML 2003, pages 145–156. Springer, 2003. [21] Christopher D Manning and Prabhakar Raghavan. Introduction to information retrieval, volume 1. [22] Maurice G Kendall. A new measure of rank correlation. Biometrika, pages 81–93, 1938. [23] Kalervo Jarvelin and Jaana Kekalainen. Cumulated gain-based evaluation of ir techniques. ACM Transactions on Information Systems (TOIS), 20(4):422–446, 2002. [24] Ricardo A. Baeza-Yates and Berthier Ribeiro-Neto. Modern Information Retrieval. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1999. [25] Christopher JC Burges. From ranknet to lambdarank to lambdamart: An overview. 2010. [26] Ralph Allan Bradley and Milton E Terry. Rank analysis of incomplete block designs: I. the method of paired comparisons. Biometrika, pages 324–345, 1952. [27] R Duncan Luce. Individual choice behavior: A theoretical analysis. Courier Dover Publications, 2005. [28] Hadi Fanaee-T and Joao Gama. Event labeling combining ensemble detectors and background knowledge. Progress in Artificial Intelligence, pages 1–15, 2013. [29] Barry Kelly Pace. Sparse spatial autoregressions, 1997. [30] Michael Revow. comp-activ dataset. [31] James Allan, Ben Carterette, Javed A Aslam, Virgil Pavlu, Blagovest Dachev, and Evangelos Kanoulas. Million query track 2007 overview. Technical report, DTIC Document, 2007. [32] Ben Carterette, Virgiliu Pavlu, Hui Fang, and Evangelos Kanoulas. Million query track 2009 overview. [33] Olivier Chapelle and Yi Chang. Yahoo! learning to rank challenge overview. [34] Sergey Bochkanov. Alglib. www.alglib.net. [35] Van Dang. Ranklib. [36] Greg Ridgeway. Generalized boosted regression models. 2006. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/56714 | - |
| dc.description.abstract | 在學習排名的方法中,有別於一般的基於分數得到排名的方法,一類基於偏好學習排名的模型先是利用二元分類模型去預測兩個待排序物件之間的偏好關係,再利用物件兩兩之間的偏好關係去產生排名。許多先前提出的偏好學習排名方法的共同問題便是在預測階段的時間效率不彰。為此,在這篇文章中,我們提出一新的分治方法 'Fuzzy Sort' 來解決偏好學習排名在預測階段的效率問題。我們的方法能在 O(W·N lg N) 的時間內完成預測,其中 W 是一可調整的參數,在一般的狀況下不超過 50。我們提出的演算法相對於其他偏好學習排名的方法,大幅改善了預測效率,並且在準確度勝過了大多數傳統基於分數得到排名的模型。 | zh_TW |
| dc.description.abstract | In preference-based learning to rank (LTR), rather than training a score- based prediction model, a binary prediction model (with probabilistic output) is trained over pairs of instances as a preference function. The ranking is then produced using the pairwise preference outputs in the prediction stage. In this paper we study the preference-based LTR problem and presents a practical approach we called the “Fuzzy Sort” which runs in O(W·N lg N), where W is typically no larger than 50 in practice. The algorithm shows promising results compared with other conventional ranking methods, and is query-efficient when competing against other preference-based LTR approaches. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T05:43:51Z (GMT). No. of bitstreams: 1 ntu-103-R01922064-1.pdf: 646028 bytes, checksum: dad37f2c2b9fadcb69f617520f5f64ee (MD5) Previous issue date: 2014 | en |
| dc.description.tableofcontents | 口試委員審定書 ... i
Acknowledgments ... ii 中文 ... iii Abstract ... iv Contents ... v List of Figures ... vii List of Tables ... viii 1. Introduction ... 1 2. Backgrounds ... 5 + 2.1 Preference-Based Learning to Rank ... + 2.2 Evaluatoin Criteria ... 6 + 2.3 Min-FAST ... 7 + 2.4 FAS-Pivot ... 7 + 2.5 Sort-by-Degree ... 8 3. Proposed Methods ... 11 + 3.1 Fuzzy Sort ... 11 + 3.2 Complexity of Fuzzy-Sort ... 13 + 3.3 Viewpoints on the Algorithm ... 14 4. Experiments ... 17 + 4.1 Comparison of Different Prediction Scheme on Artificial Data ... 17 + 4.2 Performance on Real Data ... 19 5. Conclusion ... 23 6. Remarks ... 24 Bibliography ... 25 | |
| dc.language.iso | en | |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 排名學習 | zh_TW |
| dc.subject | 偏好 | zh_TW |
| dc.subject | 分治 | zh_TW |
| dc.subject | machine learning | en |
| dc.subject | learning to rank | en |
| dc.subject | preference-based | en |
| dc.subject | divide-and-conquer | en |
| dc.title | 一基於偏好學習排名的分治方法 | zh_TW |
| dc.title | A Practical Divide-and-Conquer Approach for Preference-Based Learning to Rank | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 102-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 林守德(Lin-Shou De),Lee-Yuh Jye(李育杰) | |
| dc.subject.keyword | 機器學習,排名學習,偏好,分治, | zh_TW |
| dc.subject.keyword | machine learning,learning to rank,preference-based,divide-and-conquer, | en |
| dc.relation.page | 28 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2014-08-11 | |
| 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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