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In this paper, we investigate parallel learning to rank, targeting simultaneous improvement in accuracy and efficiency. ... Learning to rank represents a category of effective ranking methods for information retrieval. ... CCRank adapts parallel CC to learning to rank. It starts with problem decomposition, followed by a parallel iterative coevolution process. ...doi:10.1145/2009916.2010060 dblp:conf/sigir/WangGWL11 fatcat:5555rz35pnhy3gkxzqnmk2bcim
With CCRank, we investigate parallel CC in the context of learning to rank. We implement CCRank with three EA-based learning to rank algorithms for demonstration. ... We propose CCRank, the first parallel framework for learning to rank based on evolutionary algorithms (EA), aiming to significantly improve learning efficiency while maintaining accuracy. ... ACKNOWLEDGMENTS The authors would like to thank the editor and the anonymous reviewers for their constructive comments and suggestions. ...doi:10.1109/tkde.2015.2453952 fatcat:gltmjpyaencx5dnulatxppwyyy
We consider that the favoured alternative for the colleges is, with respect to the business climate, the Hybrid Cloud arrangement, which may utilize Private Clouds for the learning the board frameworks ... These practices have nothing to do with the scholarly world, and it is profoundly amusing that these college rankings are so broadly counselled, notwithstanding an absence of adherence to the most essential ... In this paper we proposed CC Rank, an equal figuring out how to rank structure dependent on helpful coevolution, planning to fundamentally improve the learning effectiveness while look after exactness. ...doi:10.22214/ijraset.2021.33391 fatcat:avivblg5r5cmlb4i5xymsgdy5e
In practice, a ranking function (or model) is exploited to determine the rank-order relations between objects, with respect to a particular criterion. ... ., query and clickthrough logs), supervised learning-based methods, referred to as "learning to rank (LTR)" methods, e.g., , Ranking SVM , ListNet , AdaRank , RankBoost  and RankNet ... the ground truth ranking and the output ranking linear CCRank  a parallel evolution framework based on cooperative coevolution MAP; NDCG non-linear THE PROPOSED GP-BASED LEARNING METHOD RankMGP ...doi:10.22452/mjcs.vol30no1.3 fatcat:y2boc575xjhu7nkw5eutgpqioi
The novel algorithm is compared with traditional rank-learning algorithms through experimentation and shown to outperform the others in respect to accuracy, learning time, and convergence rate; taken together ... This paper proposes a parallel B cell algorithm, RankBCA, for rank learning which utilizes a clonal selection mechanism based on biological immunity. ...  propose a parallel framework called CCRank for learning to rank based on evolutionary algorithms. ...doi:10.1371/journal.pone.0157994 pmid:27487242 pmcid:PMC4972358 fatcat:nbxpmvhbcrbpbawm6435w2czfq
Learning to rank is an increasingly important scientific field that comprises the use of machine learning for the ranking task. ... New learning to rank methods are generally evaluated on benchmark test collections. ... CCRank: Parallel Learning to Rank with Cooperative Coevolution. In Proceedings of the 25th AAAI Conference on Artificial Intelligence. Wang, S., Ma, J., and Liu, J. (2009b). ...doi:10.1016/j.ipm.2015.07.002 fatcat:vityxuoyxzfezhdizq7sfofwka