Results 61 to 70 of about 1,080,302 (365)
Conference Paper Recommendation for Academic Conferences
IEEE Access, 2018 With the rapid growth of scientific publications, research paper recommendation which suggests relevant research papers to users can bring great benefits to researchers.
Shuchen Li +3 more
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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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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 from medical imaging data [PDF]
, 2012 Medical images can be used to predict a clinical score coding for the severity of a disease, a pain level or the complexity of a cognitive task. In all these cases, the predicted variable has a natural order.
F. Pedregosa +11 more
core +4 more sources
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
doaj +1 more source
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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A Stochastic Treatment of Learning to Rank Scoring Functions
Web Search and Data Mining, 2020 Learning to Rank, a central problem in information retrieval, is a class of machine learning algorithms that formulate ranking as an optimization task.
Sebastian Bruch +3 more
semanticscholar +1 more source
Exploiting Unlabeled Data in CNNs by Self-Supervised Learning to Rank [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019 For many applications the collection of labeled data is expensive laborious. Exploitation of unlabeled data during training is thus a long pursued objective of machine learning.
Xialei Liu +2 more
semanticscholar +1 more source
ADAPTATION OF LAMBDAMART MODEL TO SEMI-SUPERVISED LEARNING
Вісник Національного технічного університету "ХПÌ": Системний аналіз, управління та інформаційні технології, 2023 The problem of information searching is very common in the age of the internet and Big Data. Usually, there are huge collections of documents and only multiple percent of them are relevant. In this setup brute-force methods are useless.
Klym Yamkovyi
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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
core +1 more source