Results 301 to 310 of about 1,080,302 (365)

Evaluation of data driven low-rank matrix factorization for accelerated solutions of the Vlasov equation. [PDF]

PLoS One
Jonnalagadda B, Becker S.
europepmc   +1 more source

Ensemble boosting-based soft-computing models for predicting the bond strength between steel and CFRP plate. [PDF]

Sci Rep
Afriadi I, Thongchom C, Kumar DR, Keawsawasvong S, Wipulanusat W.   +4 more
europepmc   +1 more source

DTI-RME: a robust and multi-kernel ensemble approach for drug-target interaction prediction. [PDF]

BMC Biol
Qian Y, Zhang X, Wang Y, Zou Q, Cao C, Ding Y, Guo X.   +6 more
europepmc   +1 more source

Coordinated multi-level adaptations across neocortical areas during task learning. [PDF]

Nat Commun
Han S, Helmchen F.
europepmc   +1 more source

SMOTE algorithm optimization and application in corporate credit risk prediction with diversification strategy consideration. [PDF]

Sci Rep
Wei H.
europepmc   +1 more source

Use of an Immersive Virtual Reality Application to Educate Medical Students in Patient Handover: Pilot Study.

JMIR Serious Games
Hanke LI, Schwoerer P, Huettl F, Vradelis L, Strelow KU, Boedecker C, Saalfeld P, Chheang V, Buggenhagen H, Lang H, Hansen C, Huber T.   +11 more
europepmc   +1 more source

Cortex-wide laminar dynamics diverge during learning

Pollak YE, Sachdev R, Larkum M, Gilad A.
europepmc   +1 more source

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Diversification-Aware Learning to Rank using Distributed Representation

The Web Conference, 2021
Existing work on search result diversification typically falls into the “next document” paradigm, that is, selecting the next document based on the ones already chosen.
Le Yan, Zhen Qin, Rama Kumar Pasumarthi, Xuanhui Wang, Michael Bendersky   +4 more
semanticscholar   +1 more source

An Efficient Approach for Cross-Silo Federated Learning to Rank

IEEE International Conference on Data Engineering, 2021
Traditional learning-to-rank (LTR) models are usually trained in a centralized approach based upon a large amount of data. However, with the increasing awareness of data privacy, it is harder to collect data from multiple owners as before, and the ...
Yansheng Wang, Yongxin Tong, D. Shi, Ke Xu   +3 more
semanticscholar   +1 more source

Ensemble Ranking SVM for learning to rank

2011 IEEE International Workshop on Machine Learning for Signal Processing, 2011
This paper deals with the problem of learning to rank documents for information retrieval. Until now, Ranking SVM has been successfully used for learning to rank documents. The basic idea of Ranking SVM is to formalize learning to rank as a problem of binary classification on instance pairs and solve the problem using SVM.
Cheolkon Jung, Yanbo Shen, Licheng Jiao
openaire   +2 more sources