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Active Learning for Ranking through Expected Loss Optimization

Active Learning for Ranking through Expected Loss Optimization

Starting at: Rs.4,500.00

4500 reward points

Active Learning for Ranking through Expected Loss Optimization

 Learning to rank arises in many data mining applications, ranging from web search engine, online advertising to recommendation system. In learning to rank, the performance of a ranking model is strongly affected by the number of labeled examples in the training set; on the other hand, obtaining labeled examples for training data is very expensive and time-consuming. This presents a great need for the active learning approaches to select most informative examples for ranking learning; however, in the literature there is still very limited work to address active learning for ranking. In this paper, we propose a general active learning framework, expected loss optimization (ELO), for ranking. The ELO framework is applicable to a wide range of ranking functions. Under this framework, we derive a novel algorithm, expected discounted cumulative gain (DCG) loss optimization (ELO-DCG), to select most informative examples.


 


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  • Model: PROJ5067
  • 999 Units in Stock
  • Manufactured by: ClickMyProjects

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This product was added to our catalog on Thursday 08 September, 2016.

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