Active Learning to Rank
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15.04.13 – 35445:03Популярные
20 дней – 2 2830:40
15 дней – 1 2140:53Опубликовано 15 апреля 2013, 10:26
Alexey Voropaev, ECIR 2013
Development of a system based on supervised machine learning includes three main steps: factors selection, building training set and appropriate ML algorithm application. The training set construction is the very problematic aspect, since usually it is not well controlled but may dramatically affect the resulting quality of ML model. In my talk I am going to introduce our active learning technique to manipulate the training set in context of learning to rank problem. Using simple and effective algorithm we can significantly reduce the training set size as well as improve the ranking quality.
Development of a system based on supervised machine learning includes three main steps: factors selection, building training set and appropriate ML algorithm application. The training set construction is the very problematic aspect, since usually it is not well controlled but may dramatically affect the resulting quality of ML model. In my talk I am going to introduce our active learning technique to manipulate the training set in context of learning to rank problem. Using simple and effective algorithm we can significantly reduce the training set size as well as improve the ranking quality.
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