文章基本信息

标题：ROW AND COLUMN GENERATION ALGORITHMS FOR MINIMUM MARGIN MAXIMIZATION OF RANKING PROBLEMS
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作者：Yoichi Izunaga ; Keisuke Sato ; Keiji Tatsumi 等
期刊名称：日本オペレーションズ・リサーチ学会論文誌
印刷版ISSN：0453-4514
电子版ISSN：2188-8299
出版年度：2015
卷号：58
期号：4
页码：394-409
DOI：10.15807/jorsj.58.394
出版社：Japan Science and Technology Information Aggregator, Electronic
摘要：We consider the ranking problem of learning a ranking function from the data set of objects each of which is endowed with an attribute vector and a ranking label chosen from the ordered set of labels. We propose two different formulations: primal problem, primal problem with dual representation of normal vector, and then propose to apply the kernel technique to the latter formulation. We also propose algorithms based on the row and column generation in order to mitigate the computational burden due to the large number of objects.