以資料選擇技術幫助大規模支持向量機排序

Data selection techniques for large-scale RankSVM

林庚毅(Ken-Yi Lin)

指導教授 ： 林軒田

國立臺灣大學/電機資訊學院/資訊工程學研究所/碩士(2010年)

https://doi.org/10.6342/NTU.2010.02365

摘要

無資料

關鍵字

排序問題 ； 排序學習 ； 支持向量機排序法 ； 資料選擇技術

並列摘要

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.

並列關鍵字

learning to rank ； pair-wise ranking ； RankSVM ； data selection

參考文獻

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