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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林軒田 | |
| dc.contributor.author | Ken-Yi Lin | en |
| dc.contributor.author | 林庚毅 | zh_TW |
| dc.date.accessioned | 2021-06-15T05:44:37Z | - |
| dc.date.available | 2011-08-20 | |
| dc.date.copyright | 2010-08-20 | |
| dc.date.issued | 2010 | |
| dc.date.submitted | 2010-08-19 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/46983 | - |
| dc.description.abstract | Learning to rank has become a popular research topic in several areas including
information retrieval and machine learning. Pair-wise ranking, which learns all the order preferences between every two examples, is one typical method for solving the ranking problem. In pair-wise ranking, RankSVM is a widely-used machine learning algorithm and has been successfully applied to the ranking problem in the previous work. However, RankSVM suffers a critical problem which is the long training time because of the huge number of pairs. In this thesis, we propose a data selection technique, Pruned RankSVM, that selects the most informative pairs before training. If we use partial pairs instead of total ones, we can train a large-scale data set efficiently. In the experiment, we show the performance of Pruned RankSVM is overall comparable with RankSVM while using significantly fewer pairs. To show the efficiency of Pruned RankSVM, we also compare with one point-wise ranking algorithm : support vector regression. Experimental results demonstrate that Pruned RankSVM outperforms support vector regression on most data sets. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T05:44:37Z (GMT). No. of bitstreams: 1 ntu-99-R97922117-1.pdf: 2963993 bytes, checksum: 5f8689b37c0493ed05b00db62b97016e (MD5) Previous issue date: 2010 | en |
| dc.description.tableofcontents | 致謝i
中文摘要iii Abstract v 1 Introduction 1 1.1 Ranking Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.4 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 Pair-wise Ranking 5 2.1 Existing Pair-wise Ranking Algorithms . . . . . . . . . . . . . . . . . . 5 2.1.1 RankSVM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1.2 RankLR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2 Problem of Pair-wise Ranking Algorithms . . . . . . . . . . . . . . . . . 9 2.3 Why RankSVM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3 Pruned RankSVM with Closest Pairs 11 3.1 Informative Pairs in RankSVM . . . . . . . . . . . . . . . . . . . . . . . 11 3.1.1 Support Vector Distribution . . . . . . . . . . . . . . . . . . . . 14 3.1.2 Important Pairs during Optimization . . . . . . . . . . . . . . . . 15 3.2 Pruned RankSVM (closest) . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.2.1 Experiment on Artificial Data . . . . . . . . . . . . . . . . . . . 18 3.2.2 Experiment on Real-world Data . . . . . . . . . . . . . . . . . . 20 3.3 Extended Approach with Numerical Information . . . . . . . . . . . . . 21 3.3.1 Order-based versus Label-based . . . . . . . . . . . . . . . . . . 22 3.3.2 Emphasis on Label-closest Pairs . . . . . . . . . . . . . . . . . . 23 3.3.3 Emphasis on Label-farthest Pairs . . . . . . . . . . . . . . . . . . 24 3.3.4 Comparison with Pruned RankSVM (closest) . . . . . . . . . . . 25 4 Pruned RankSVM with Mixed Pairs 29 4.1 Pruned RankSVM (mixed) . . . . . . . . . . . . . . . . . . . . . . . . . 29 4.1.1 Pruned RankSVM (mixed) on Artificial Data . . . . . . . . . . . 30 4.1.2 Pruned RankSVM (mixed) on Real-world Data . . . . . . . . . . 31 4.2 Comparison between Closest and Mixed Technique . . . . . . . . . . . . 32 4.3 Coupling Pruned RankSVM (mixed) with Shrinking . . . . . . . . . . . 32 vii 5 Conclusion 35 Bibliography 37 viii | |
| dc.language.iso | en | |
| dc.subject | 支持向量機排序法 | zh_TW |
| dc.subject | 排序學習 | zh_TW |
| dc.subject | 排序問題 | zh_TW |
| dc.subject | 資料選擇技術 | zh_TW |
| dc.subject | data selection | en |
| dc.subject | RankSVM | en |
| dc.subject | pair-wise ranking | en |
| dc.subject | learning to rank | en |
| dc.title | 以資料選擇技術幫助大規模支持向量機排序 | zh_TW |
| dc.title | Data selection techniques for large-scale RankSVM | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 98-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 徐宏民,李育杰,王鈺強 | |
| dc.subject.keyword | 排序問題,排序學習,支持向量機排序法,資料選擇技術, | zh_TW |
| dc.subject.keyword | learning to rank,pair-wise ranking,RankSVM,data selection, | en |
| dc.relation.page | 40 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2010-08-19 | |
| 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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