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In machine learning, a Ranking SVM is an application of Support vector machine, which is used to solve certain ranking problems. The algorithm of ranking SVM was published by Thorsten Joachims in 2003.〔Joachims, T. (2003), "Optimizing Search Engines using Clickthrough Data", Proceedings of the ACM Conference on Knowledge Discovery and Data Mining〕 The original purpose of Ranking SVM is to improve the performance of an internet search engine. However, it was found that Ranking SVM also can be used to solve other problems such as Rank SIFT.〔Bing Li; Rong Xiao; Zhiwei Li; Rui Cai; Bao-Liang Lu; Lei Zhang; "Rank-SIFT: Learning to rank repeatable local interest points",Computer Vision and Pattern Recognition (CVPR), 2011〕 ==Description== Ranking SVM, one of the pair-wise ranking methods, which is used to adaptively sort the web-pages by their relationships (how relevant) to a specific query. A mapping function is required to define such relationship. The mapping function projects each data pair (inquire and clicked web-page) onto a feature space. These features combined with user’s click-through data (which implies page ranks for a specific query) can be considered as the training data for machine learning algorithms. Generally, Ranking SVM includes three steps in the training period: # It maps the similarities between queries and the clicked pages onto certain feature space. # It calculates the distances between any two of the vectors obtained in step 1. # It forms optimization problem which is similar to SVM classification and solves such problem with the regular SVM solver. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Ranking SVM」の詳細全文を読む スポンサード リンク
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