Results 41 to 50 of about 1,093,151 (324)
Learning to Rank Sports Teams on a Graph
Applied Sciences, 2020 To improve the prediction ability of ranking models in sports, a generalized PageRank model is introduced. In the model, a game graph is constructed from the perspective of Bayesian correction with game results. In the graph, nodes represent teams, and a
Jian Shi, Xin-Yu Tian
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Pairwise Learning to Rank for Image Quality Assessment
IEEE Access, 2020 Because the pairwise comparison is a natural and effective way to obtain subjective image quality scores, we propose an objective full-reference image quality assessment (FR-IQA) index based on pairwise learning to rank (PLR).
Yiqing Shi +4 more
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Recommending GitHub Projects for Developer Onboarding
IEEE Access, 2018 Open-source platform (e.g., GitHub) creates a tremendous opportunity for developers to learn and build experience. Contribution to open source can be rewarding for developers and advocates the evolutionary progress of the open-source software.
Chao Liu +4 more
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Ranking Algorithms for Word Ordering in Surface Realization
Information, 2021 In natural language generation, word ordering is the task of putting the words composing the output surface form in the correct grammatical order. In this paper, we propose to apply general learning-to-rank algorithms to the task of word ordering in the ...
Alessandro Mazzei +3 more
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Learning to Rank based on Analogical Reasoning
, 2017 Object ranking or "learning to rank" is an important problem in the realm of preference learning. On the basis of training data in the form of a set of rankings of objects represented as feature vectors, the goal is to learn a ranking function that ...
Fahandar, Mohsen Ahmadi +1 more
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Sensors, 2022 When learning to play a musical instrument, it is important to improve the quality of self-practice. Many systems have been developed to assist practice. Some practice assistance systems use special sensors (pressure, flow, and motion sensors) to acquire
Jin Kuroda, Gou Koutaki
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Learning to efficiently rank [PDF]
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, 2010 It has been shown that learning to rank approaches are capable of learning highly effective ranking functions. However, these approaches have mostly ignored the important issue of efficiency. Given that both efficiency and effectiveness are important for real search engines, models that are optimized for effectiveness may not meet the strict efficiency
Lidan Wang, Jimmy Lin, Donald Metzler
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Hashing as Tie-Aware Learning to Rank
, 2018 Hashing, or learning binary embeddings of data, is frequently used in nearest neighbor retrieval. In this paper, we develop learning to rank formulations for hashing, aimed at directly optimizing ranking-based evaluation metrics such as Average Precision
Bargal, Sarah Adel +3 more
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Learning to Rank Learning Curves
, 2020 Many automated machine learning methods, such as those for hyperparameter and neural architecture optimization, are computationally expensive because they involve training many different model configurations. In this work, we present a new method that saves computational budget by terminating poor configurations early on in the training. In contrast to
Wistuba, Martin, Pedapati, Tejaswini
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Learning to Rank Using Localized Geometric Mean Metrics
, 2017 Many learning-to-rank (LtR) algorithms focus on query-independent model, in which query and document do not lie in the same feature space, and the rankers rely on the feature ensemble about query-document pair instead of the similarity between query ...
King, Irwin, Lyu, Michael, Su, Yuxin
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