Academic literature on the topic 'Ranking learning'

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Journal articles on the topic "Ranking learning"

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Guo, Qianyu, Gong Haotong, Xujun Wei, et al. "RankDNN: Learning to Rank for Few-Shot Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1 (2023): 728–36. http://dx.doi.org/10.1609/aaai.v37i1.25150.

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This paper introduces a new few-shot learning pipeline that casts relevance ranking for image retrieval as binary ranking relation classification. In comparison to image classification, ranking relation classification is sample efficient and domain agnostic. Besides, it provides a new perspective on few-shot learning and is complementary to state-of-the-art methods. The core component of our deep neural network is a simple MLP, which takes as input an image triplet encoded as the difference between two vector-Kronecker products, and outputs a binary relevance ranking order. The proposed RankML
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Dzyuba, Vladimir, Matthijs van Leeuwen, Siegfried Nijssen, and Luc De Raedt. "Interactive Learning of Pattern Rankings." International Journal on Artificial Intelligence Tools 23, no. 06 (2014): 1460026. http://dx.doi.org/10.1142/s0218213014600264.

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Pattern mining provides useful tools for exploratory data analysis. Numerous efficient algorithms exist that are able to discover various types of patterns in large datasets. Unfortunately, the problem of identifying patterns that are genuinely interesting to a particular user remains challenging. Current approaches generally require considerable data mining expertise or effort from the data analyst, and hence cannot be used by typical domain experts. To address this, we introduce a generic framework for interactive learning of userspecific pattern ranking functions. The user is only asked to
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Hiemstra, Djoerd. "Was Fairness in IR Discussed by Cooper and Robertson in the 1970's?" ACM SIGIR Forum 56, no. 2 (2022): 1–5. http://dx.doi.org/10.1145/3582900.3582924.

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I discuss fairness in Information Retrieval (IR) through the eyes of Cooper and Robertson's probability ranking principle. I argue that unfair rankings may arise from blindly applying the principle without checking whether its preconditions are met. Following this argument, unfair rankings originate from the application of learning-to-rank approaches in cases where they should not be applied according to the probability ranking principle. I use two examples to show that fairer rankings may also be more relevant than rankings that are based on the probability ranking principle.
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Yu, Hai-Tao, Degen Huang, Fuji Ren, and Lishuang Li. "Diagnostic Evaluation of Policy-Gradient-Based Ranking." Electronics 11, no. 1 (2021): 37. http://dx.doi.org/10.3390/electronics11010037.

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Learning-to-rank has been intensively studied and has shown significantly increasing values in a wide range of domains, such as web search, recommender systems, dialogue systems, machine translation, and even computational biology, to name a few. In light of recent advances in neural networks, there has been a strong and continuing interest in exploring how to deploy popular techniques, such as reinforcement learning and adversarial learning, to solve ranking problems. However, armed with the aforesaid popular techniques, most studies tend to show how effective a new method is. A comprehensive
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Poplavskyi, Mykhailo. "University Ranking As an Education Quality Assessment Tool." Ukrainian Information Space, no. 2(8) (November 15, 2021): 16–38. https://doi.org/10.31866/2616-7948.2(8).2021.245787.

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The purpose of the article is a comprehensive analysis of the ranking methodology of leading international and European University rankings. The research methodology is based on abstraction use, analysis, and synthesis methods, which have provided theoretical comprehension for the phenomenon of world universities ranking. The scientific novelty of the findings involves proposal development for upgrading International University Rankings. Based on the analysis of rates, indicators, and weight assignment of the most influential International University Rankings in the world (ARWU, THE, QS World
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Sumanth Reddy, Shiva, Anil Kumar B, Jahnavi S, Manjunath D R, Girish N, and Nandini C. "Enhancing Academic Excellence through Autonomy: A Data-Driven Analysis of NIRF Rankings in South Indian Higher Educational Institutions." Journal of Hacking Techniques, Digital Crime Prevention and Computer Virology 1, no. 3 (2025): 36–49. https://doi.org/10.46610/johtdcpcv.2024.v01i03.004.

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Published as part of the research "Impact on NIRF Rankings", this study examines how colleges in Andhra Pradesh, Tamil Nadu, Kerala, and Telangana are performing regarding their autonomous status under the National Institutional Ranking Framework (NIRF). Using a detailed dataset from 2016–2023, the study employs advanced statistical techniques, including descriptive statistics, paired t-tests, Wilcoxonsigned-rank tests, and multiple regression models to uncover differences in rankings of NIRF before and after autonomy.Time series analysis with trend lines and moving averages can identify signi
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Pan, Weike, Qiang Yang, Yuchao Duan, Ben Tan, and Zhong Ming. "Transfer Learning for Behavior Ranking." ACM Transactions on Intelligent Systems and Technology 8, no. 5 (2017): 1–23. http://dx.doi.org/10.1145/3057732.

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Jiang, Liangxiao. "Learning random forests for ranking." Frontiers of Computer Science in China 5, no. 1 (2010): 79–86. http://dx.doi.org/10.1007/s11704-010-0388-5.

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Geng, Xiubo, and Xue-Qi Cheng. "Learning multiple metrics for ranking." Frontiers of Computer Science in China 5, no. 3 (2011): 259–67. http://dx.doi.org/10.1007/s11704-011-0152-5.

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Rahangdale, Ashwini, and Shital Raut. "Machine Learning Methods for Ranking." International Journal of Software Engineering and Knowledge Engineering 29, no. 06 (2019): 729–61. http://dx.doi.org/10.1142/s021819401930001x.

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Learning-to-rank is one of the learning frameworks in machine learning and it aims to organize the objects in a particular order according to their preference, relevance or ranking. In this paper, we give a comprehensive survey for learning-to-rank. First, we discuss the different approaches along with different machine learning methods such as regression, SVM, neural network-based, evolutionary, boosting method. In order to compare different approaches: we discuss the characteristics of each approach. In addition to that, learning-to-rank algorithms combine with other machine learning paradig
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Dissertations / Theses on the topic "Ranking learning"

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Latham, Andrew C. "Multiple-Instance Feature Ranking." Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1440642294.

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Sinsel, Erik W. "Ensemble learning for ranking interesting attributes." Morgantown, W. Va. : [West Virginia University Libraries], 2005. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4400.

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Thesis (M.S.)--West Virginia University, 2005.<br>Title from document title page. Document formatted into pages; contains viii, 81 p. : ill. Includes abstract. Includes bibliographical references (p. 72-74).
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Mattsson, Fredrik, and Anton Gustafsson. "Optimize Ranking System With Machine Learning." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-37431.

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This thesis investigates how recommendation systems has been used and can be used with the help of different machine learning algorithms. Algorithms used and presented are decision tree, random forest and singular-value decomposition(SVD). Together with Tingstad, we have tried to implement the SVD function on their recommendation engine in order to enhance the recommendation given. A trivial presentation on how the algorithms work. General information about machine learning and how we tried to implement it with Tingstad’s data. Implementations with Netflix’s and Movielens open-source dataset w
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Achab, Mastane. "Ranking and risk-aware reinforcement learning." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAT020.

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Les travaux de cette thèse se situent à l’interface de deux thématiques de l'apprentissage automatique : l’apprentissage de préférences d'une part, et l’apprentissage par renforcement de l'autre. La première consiste à percoler différents classements d’un même ensemble d’objets afin d’en extraire un ordre général, la seconde à identifier séquentiellement une stratégie optimale en observant des récompenses sanctionnant chaque action essayée. La structure de la thèse suit ce découpage thématique. En première partie, le paradigme de minimisation du risque empirique est utilisé à des fins d'ordonn
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Korba, Anna. "Learning from ranking data : theory and methods." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLT009/document.

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Les données de classement, c.à. d. des listes ordonnées d'objets, apparaissent naturellement dans une grande variété de situations, notamment lorsque les données proviennent d’activités humaines (bulletins de vote d'élections, enquêtes d'opinion, résultats de compétitions) ou dans des applications modernes du traitement de données (moteurs de recherche, systèmes de recommendation). La conception d'algorithmes d'apprentissage automatique, adaptés à ces données, est donc cruciale. Cependant, en raison de l’absence de structure vectorielle de l’espace des classements et de sa cardinalité explosiv
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Korba, Anna. "Learning from ranking data : theory and methods." Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLT009.

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Les données de classement, c.à. d. des listes ordonnées d'objets, apparaissent naturellement dans une grande variété de situations, notamment lorsque les données proviennent d’activités humaines (bulletins de vote d'élections, enquêtes d'opinion, résultats de compétitions) ou dans des applications modernes du traitement de données (moteurs de recherche, systèmes de recommendation). La conception d'algorithmes d'apprentissage automatique, adaptés à ces données, est donc cruciale. Cependant, en raison de l’absence de structure vectorielle de l’espace des classements et de sa cardinalité explosiv
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FILHO, FRANCISCO BENJAMIM. "RANKING OF WEB PAGES BY LEARNING MULTIPLE LATENT CATEGORIES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2012. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=19540@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>O crescimento explosivo e a acessibilidade generalizada da World Wide Web (WWW) levaram ao aumento da atividade de pesquisa na área da recuperação de informação para páginas Web. A WWW é um rico e imenso ambiente em que as páginas se assemelham a uma comunidade grande de elementos conectada através de hiperlinks em razão da semelhança entre o conteúdo das páginas, a popularidade da página, a autoridade sobre o assunto e assim por diante, sabendo-se que, em verdade, quando um autor de uma página a vincula à outra, está concebendo-a como impo
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Cheung, Chi-Wai. "Probabilistic rank aggregation for multiple SVM ranking /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?CSED%202009%20CHEUNG.

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Vogel, Robin. "Similarity ranking for biometrics : theory and practice." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAT031.

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L’augmentation rapide de la population combinée à la mobilité croissante des individus a engendré le besoin de systèmes de gestion d’identités sophistiqués. À cet effet, le terme biométrie se réfère généralement aux méthodes permettant d’identifier les individus en utilisant des caractéristiques biologiques ou comportementales. Les méthodes les plus populaires, c’est-à-dire la reconnaissance d’empreintes digitales, d’iris ou de visages, se basent toutes sur des méthodes de vision par ordinateur. L’adoption de réseaux convolutifs profonds, rendue possible par le calcul générique sur processeur
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Zacharia, Giorgos 1974. "Regularized algorithms for ranking, and manifold learning for related tasks." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/47753.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.<br>Includes bibliographical references (leaves 119-127).<br>This thesis describes an investigation of regularized algorithms for ranking problems for user preferences and information retrieval problems. We utilize regularized manifold algorithms to appropriately incorporate data from related tasks. This investigation was inspired by personalization challenges in both user preference and information retrieval ranking problems. We formulate the ranking problem of related tasks as a
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Books on the topic "Ranking learning"

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Hawkins, John N., Aki Yamada, Reiko Yamada, and W. James Jacob, eds. New Directions of STEM Research and Learning in the World Ranking Movement. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98666-1.

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United States. Government Accountability Office. Military education: DOD needs to develop performance goals and metrics for advanced distributed learning in professional military education : report to the Ranking Minority Member, Committee on Armed Services, House of Representatives. U.S. Government Accountability Office, 2004.

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Office, General Accounting. Student financial aid: Federal aid awarded to students taking remedial courses : report to the ranking minority member, Subcommittee on Postsecondary Education, Training, and Life-Long Learning, Committee on Education and the Workforce, House of Representatives. The Office, 1997.

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Office, General Accounting. Student financial aid: Federal aid awarded to students taking remedial courses : report to the ranking minority member, Subcommittee on Postsecondary Education, Training, and Life-Long Learning, Committee on Education and the Workforce, House of Representatives. The Office, 1997.

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Xia, Lirong, Ronald Brachman, Francesca Rossi, and Peter Stone. Learning and Decision-Making from Rank Data. Morgan & Claypool Publishers, 2019.

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Xia, Lirong, Ronald Brachman, Francesca Rossi, and Peter Stone. Learning and Decision-Making from Rank Data. Morgan & Claypool Publishers, 2019.

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Xia, Lirong, Ronald Brachman, Francesca Rossi, and Peter Stone. Learning and Decision-Making from Rank Data. Morgan & Claypool Publishers, 2019.

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Jarosz, Gaja. Learning with Violable Constraints. Edited by Jeffrey L. Lidz, William Snyder, and Joe Pater. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199601264.013.30.

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This chapter provides a broad overview of recent research on constraint-based learning of phonology. The review covers the major results and the contributions of a wide range of approaches, comparing the computational properties and learning implications of these theories. Specifically, the review encompasses learning in classic OT as well as learning in related frameworks that formalize constraint interaction as (probabilistic) weighting or ranking. The learning problem is decomposed into several subproblems that highlight the complexity of the learning problem facing the child learner. The d
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Hawkins, John N., W. James Jacob, Reiko Yamada, and Aki Yamada. New Directions of STEM Research and Learning in the World Ranking Movement: A Comparative Perspective. Palgrave Macmillan, 2019.

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Hawkins, John N., W. James Jacob, Reiko Yamada, and Aki Yamada. New Directions of STEM Research and Learning in the World Ranking Movement: A Comparative Perspective. Palgrave Macmillan, 2018.

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Book chapters on the topic "Ranking learning"

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Joshi, Ameet V. "Ranking." In Machine Learning and Artificial Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26622-6_20.

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Joshi, Ameet V. "Ranking." In Machine Learning and Artificial Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12282-8_20.

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Cossock, David, and Tong Zhang. "Subset Ranking Using Regression." In Learning Theory. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11776420_44.

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Liu, Tie-Yan. "Relational Ranking." In Learning to Rank for Information Retrieval. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-14267-3_6.

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Liu, Tie-Yan. "Transfer Ranking." In Learning to Rank for Information Retrieval. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-14267-3_9.

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Kamishima, Toshihiro, and Shotaro Akaho. "Dimension Reduction for Object Ranking." In Preference Learning. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14125-6_10.

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Vembu, Shankar, and Thomas Gärtner. "Label Ranking Algorithms: A Survey." In Preference Learning. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14125-6_3.

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Zhang, Jianping, Jerzy W. Bala, Ali Hadjarian, and Brent Han. "Ranking Cases with Classification Rules." In Preference Learning. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14125-6_8.

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Agarwal, Shivani, and Dan Roth. "Learnability of Bipartite Ranking Functions." In Learning Theory. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11503415_2.

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Rendle, Steffen. "Learning Context-Aware Ranking." In Context-Aware Ranking with Factorization Models. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16898-7_4.

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Conference papers on the topic "Ranking learning"

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Huang, Hailin, Liuwen Cao, Jiexin Wang, Tianchen Yu, and Yi Cai. "Code Ranking with Structure Awareness Contrastive Learning." In 2025 IEEE/ACM 33rd International Conference on Program Comprehension (ICPC). IEEE, 2025. https://doi.org/10.1109/icpc66645.2025.00053.

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Hijazi, Sherin, Mahmoud Moshref, and Samer Mohammed Arqawi. "Evaluating Factors Influencing University Ranking Based on QS Ranking 2023-2024 Using Machine Learning Algorithms." In 2025 1st International Conference on Computational Intelligence Approaches and Applications (ICCIAA). IEEE, 2025. https://doi.org/10.1109/icciaa65327.2025.11013585.

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Chew, Min Min, Sourav S. Bhowmick, and Adam Jatowt. "Ranking Without Learning." In SIGIR '18: The 41st International ACM SIGIR conference on research and development in Information Retrieval. ACM, 2018. http://dx.doi.org/10.1145/3209978.3210100.

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Ouyang, Hua, and Alex Gray. "Learning dissimilarities by ranking." In the 25th international conference. ACM Press, 2008. http://dx.doi.org/10.1145/1390156.1390248.

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FALKOWSKI, BERND-JÜRGEN. "RANKING AND PERCEPTRON LEARNING." In Proceedings of the 9th International FLINS Conference. WORLD SCIENTIFIC, 2010. http://dx.doi.org/10.1142/9789814324700_0077.

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Clémençon, Stéphan, Marine Depecker, and Nicolas Vayatis. "Bagging Ranking Trees." In 2009 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2009. http://dx.doi.org/10.1109/icmla.2009.14.

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Roussinov, D., and Weiguo Fan. "Learning Ranking vs. Modeling Relevance." In Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06). IEEE, 2006. http://dx.doi.org/10.1109/hicss.2006.252.

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Costa, Miguel, Francisco Couto, and Mário Silva. "Learning temporal-dependent ranking models." In SIGIR '14: The 37th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2014. http://dx.doi.org/10.1145/2600428.2609619.

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Lyubchyk, Leonid, and Galyna Grinberg. "Online Ranking Learning on Clusters." In 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP). IEEE, 2018. http://dx.doi.org/10.1109/dsmp.2018.8478520.

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Chang, Xiao, and Qinghua Zheng. "Sparse Bayesian learning for ranking." In 2009 IEEE International Conference on Granular Computing (GRC). IEEE, 2009. http://dx.doi.org/10.1109/grc.2009.5255164.

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Reports on the topic "Ranking learning"

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Berkhout, Emilie, Goldy Dharmawan, Amanda Beatty, Daniel Suryadarma, and Menno Pradhan. Who Benefits and Loses from Large Changes to Student Composition? Assessing Impacts of Lowering School Admissions Standards in Indonesia. Research on Improving Systems of Education (RISE), 2022. http://dx.doi.org/10.35489/bsg-risewp_2022/094.

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We study the effects of an admission policy change that caused a massive shift in student composition in public and private junior secondary schools in Yogyakarta, Indonesia. In 2018, the primary criterion for admission into Yogyakarta’s 16 preferred, free public schools (grades 7-9) changed from a grade 6 exam score ranking to a neighborhood-to-school distance ranking. This policy change resulted in a decline in average grade 6 scores in public schools by 0.4 standard deviations (s.d.) and a 0.4 s.d. increase in private schools. We assessed learning impacts caused by the changed student compo
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Berkhout, Emilie, Goldy Dharmawan, Amanda Beatty, Daniel Suryadarma, and Menno Pradhan. Who Benefits and Loses from Large Changes to Student Composition? Assessing Impacts of Lowering School Admissions Standards in Indonesia. Research on Improving Systems of Education (RISE), 2022. http://dx.doi.org/10.35489/bsg-risewp_2022/094.

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We study the effects of an admission policy change that caused a massive shift in student composition in public and private junior secondary schools in Yogyakarta, Indonesia. In 2018, the primary criterion for admission into Yogyakarta’s 16 preferred, free public schools (grades 7-9) changed from a grade 6 exam score ranking to a neighborhood-to-school distance ranking. This policy change resulted in a decline in average grade 6 scores in public schools by 0.4 standard deviations (s.d.) and a 0.4 s.d. increase in private schools. We assessed learning impacts caused by the changed student compo
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Schiefelbein, Ernesto, Paulina Schiefelbein, and Laurence Wolff. Cost-Effectiveness of Education Policies in Latin America: A Survey of Expert Opinion. Inter-American Development Bank, 1998. http://dx.doi.org/10.18235/0008789.

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This paper provides an alternative approach to measuring the cost-effectiveness of educational interventions. The authors devised a questionnaire and gave it to ten international experts, mainly located in universities and international agencies, all of whom were well acquainted with educational research and with practical attempts at educational reform in the region; as well as to about 30 Latin American planner/practitioners, most of them working in the planning office of their ministry of education. Each respondent was asked to estimate the impact of 40 possible primary school interventions
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Sandford, Robert, Vladimir Smakhtin, Colin Mayfield, et al. Canada in the Global Water World: Analysis of Capabilities. United Nations University Institute for Water, Environment and Health, 2018. http://dx.doi.org/10.53328/vsgg2030.

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This report critically examines, for the first time, the capacity of Canada’s water sector with respect to meeting and helping other countries meet the water-related targets of the UN’s global sustainable development agenda. Several components of this capacity are examined, including water education and research, investment in water projects that Canada makes internally and externally, and experiences in water technology and governance. Analysis of the water education system suggests that there is a broad capability in institutions of higher learning in Canada to offer training in the diverse
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