https://scholars.lib.ntu.edu.tw/handle/123456789/488544
標題: | Data Selection Techniques for Large-Scale Rank SVM. | 作者: | Lin, Ken-Yi Jan, Te-Kang HSUAN-TIEN LIN |
關鍵字: | data selection technique; learning to rank; pair-wise ranking; RankSVM | 公開日期: | 2013 | 起(迄)頁: | 25-30 | 來源出版物: | Conference on Technologies and Applications of Artificial Intelligence, TAAI 2013, Taipei, Taiwan, December 6-8, 2013 | 摘要: | Learning to rank has become a popular research topic in several areas such as information retrieval and machine learning. Pair-wise ranking, which learns all the order preferences between pairs of examples, is a typical method for solving the ranking problem. In pair-wise ranking, Rank SVM is a widely-used algorithm and has been successfully applied to the ranking problem in the previous work. However, Rank SVM suffers from the critical problem of long training time needed to deal with a huge number of pairs. In this paper, we propose a data selection technique, Pruned Rank SVM, that selects the most informative pairs before training. Experimental results show that the performance of Pruned Rank SVM is on par with Rank SVM while using significantly fewer pairs. © 2013 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84899424228&doi=10.1109%2fTAAI.2013.19&partnerID=40&md5=58b64d928aa11c27125753767b2ce8d0 | DOI: | 10.1109/TAAI.2013.19 | SDG/關鍵字: | Data reduction; Critical problems; Data Selection; Learning to rank; pair-wise ranking; Ranking problems; RankSVM; Research topics; Training time; Problem solving |
顯示於: | 資訊工程學系 |
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