Relevant bibliographies by topics / Ranking learning

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Ranking learning'

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Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "Ranking learning"

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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 (December 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 rank small sets of patterns, while a ranking function is inferred from this feedback by preference learning techniques. Moreover, we propose a number of active learning heuristics to minimize the effort required from the user, while ensuring that accurate rankings are obtained. We show how the learned ranking functions can be used to mine new, more interesting patterns. We demonstrate two concrete instances of our framework for two different pattern mining tasks, frequent itemset mining and subgroup discovery. We empirically evaluate the capacity of the algorithm to learn pattern rankings by emulating users. Experiments demonstrate that the system is able to learn accurate rankings, and that the active learning heuristics help reduce the required user effort. Furthermore, using the learned ranking functions as search heuristics allows discovering patterns of higher quality than those in the initial set. This shows that machine learning techniques in general, and active preference learning in particular, are promising building blocks for interactive data mining systems.
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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 (December 23, 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 comparison between techniques and an in-depth analysis of their deficiencies are somehow overlooked. This paper is motivated by the observation that recent ranking methods based on either reinforcement learning or adversarial learning boil down to policy-gradient-based optimization. Based on the widely used benchmark collections with complete information (where relevance labels are known for all items), such as MSLRWEB30K and Yahoo-Set1, we thoroughly investigate the extent to which policy-gradient-based ranking methods are effective. On one hand, we analytically identify the pitfalls of policy-gradient-based ranking. On the other hand, we experimentally compare a wide range of representative methods. The experimental results echo our analysis and show that policy-gradient-based ranking methods are, by a large margin, inferior to many conventional ranking methods. Regardless of whether we use reinforcement learning or adversarial learning, the failures are largely attributable to the gradient estimation based on sampled rankings, which significantly diverge from ideal rankings. In particular, the larger the number of documents per query and the more fine-grained the ground-truth labels, the greater the impact policy-gradient-based ranking suffers. Careful examination of this weakness is highly recommended for developing enhanced methods based on policy gradient.
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Hüllermeier, Eyke, and Johannes Fürnkranz. "Editorial: Preference learning and ranking." Machine Learning 93, no. 2-3 (August 31, 2013): 185–89. http://dx.doi.org/10.1007/s10994-013-5414-z.

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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 (September 27, 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 (December 4, 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 (May 6, 2011): 259–67. http://dx.doi.org/10.1007/s11704-011-0152-5.

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Jiang, Liangxiao, Chaoqun Li, and Zhihua Cai. "Learning decision tree for ranking." Knowledge and Information Systems 20, no. 1 (October 17, 2008): 123–35. http://dx.doi.org/10.1007/s10115-008-0173-z.

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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 (June 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 paradigms such as semi-supervised learning, active learning, reinforcement learning and deep learning. The learning-to-rank models employ with parallel or big data analytics to review computational and storage advantage. Many real-time applications use learning-to-rank for preference learning. In regard to this, we introduce some representative works. Finally, we highlighted future directions to investigate learning-to-rank methods.
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Ferreira, Kris J., Sunanda Parthasarathy, and Shreyas Sekar. "Learning to Rank an Assortment of Products." Management Science 68, no. 3 (March 2022): 1828–48. http://dx.doi.org/10.1287/mnsc.2021.4130.

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We consider the product-ranking challenge that online retailers face when their customers typically behave as “window shoppers.” They form an impression of the assortment after browsing products ranked in the initial positions and then decide whether to continue browsing. We design online learning algorithms for product ranking that maximize the number of customers who engage with the site. Customers’ product preferences and attention spans are correlated and unknown to the retailer; furthermore, the retailer cannot exploit similarities across products, owing to the fact that the products are not necessarily characterized by a set of attributes. We develop a class of online learning-then-earning algorithms that prescribe a ranking to offer each customer, learning from preceding customers’ clickstream data to offer better rankings to subsequent customers. Our algorithms balance product popularity with diversity, the notion of appealing to a large variety of heterogeneous customers. We prove that our learning algorithms converge to a ranking that matches the best-known approximation factors for the offline, complete information setting. Finally, we partner with Wayfair — a multibillion-dollar home goods online retailer — to estimate the impact of our algorithms in practice via simulations using actual clickstream data, and we find that our algorithms yield a significant increase (5–30%) in the number of customers that engage with the site. This paper was accepted by J. George Shanthikumar, Management Science Special Section on Data-Driven Prescriptive Analytics.
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Ochoa, X., and E. Duval. "Relevance Ranking Metrics for Learning Objects." IEEE Transactions on Learning Technologies 1, no. 1 (January 2008): 34–48. http://dx.doi.org/10.1109/tlt.2008.1.

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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.
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 was done, estimated with RMSE and MAE.
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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'ordonnancement. Partant du problème d’apprentissage supervisé de règles d’ordonnancement à partir de données étiquetées de façon binaire, une extension est proposée au cas où les étiquettes prennent des valeurs continues. Les critères de performance usuels dans le cas binaire, à savoir la courbe caractéristique de l’opérateur de réception (COR) et l’aire sous la courbe COR (ASC), sont étendus au cas continu : les métriques COR intégrée (CORI) et ASC intégrée (ASCI) sont introduites à cet effet. Le second problème d'ordonnancement étudié est celui de l'agrégation de classements à travers l'identification du consensus de Kemeny. En particulier, une relaxation au problème plus général de la réduction de la dimensionnalité dans l'espace des distributions sur le groupe symétrique est formulée à l'aide d'outils mathématiques empruntés à la théorie du transport optimal. La seconde partie de cette thèse s'intéresse à l'apprentissage par renforcement. Des problèmes de bandit manchot sont analysés dans des contextes où la performance moyenne n'est pas pertinente et où la gestion du risque prévaut. Enfin, le problème plus général de l'apprentissage par renforcement distributionnel, dans lequel le décideur cherche à connaître l'entière distribution de sa performance et non pas uniquement sa valeur moyenne, est considéré. De nouveaux opérateurs de programmation dynamique ainsi que leurs pendants atomiques mènent à de nouveaux algorithmes stochastiques distributionnels
This thesis divides into two parts: the first part is on ranking and the second on risk-aware reinforcement learning. While binary classification is the flagship application of empirical risk minimization (ERM), the main paradigm of machine learning, more challenging problems such as bipartite ranking can also be expressed through that setup. In bipartite ranking, the goal is to order, by means of scoring methods, all the elements of some feature space based on a training dataset composed of feature vectors with their binary labels. This thesis extends this setting to the continuous ranking problem, a variant where the labels are taking continuous values instead of being simply binary. The analysis of ranking data, initiated in the 18th century in the context of elections, has led to another ranking problem using ERM, namely ranking aggregation and more precisely the Kemeny's consensus approach. From a training dataset made of ranking data, such as permutations or pairwise comparisons, the goal is to find the single "median permutation" that best corresponds to a consensus order. We present a less drastic dimensionality reduction approach where a distribution on rankings is approximated by a simpler distribution, which is not necessarily reduced to a Dirac mass as in ranking aggregation.For that purpose, we rely on mathematical tools from the theory of optimal transport such as Wasserstein metrics. The second part of this thesis focuses on risk-aware versions of the stochastic multi-armed bandit problem and of reinforcement learning (RL), where an agent is interacting with a dynamic environment by taking actions and receiving rewards, the objective being to maximize the total payoff. In particular, a novel atomic distributional RL approach is provided: the distribution of the total payoff is approximated by particles that correspond to trimmed means
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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 explosive lorsque le nombre d'objets augmente, la plupart des méthodes classiques issues des statistiques et de l’analyse multivariée ne peuvent être appliquées directement. Par conséquent, la grande majorité de la littérature repose sur des modèles paramétriques. Dans cette thèse, nous proposons une théorie et des méthodes non paramétriques pour traiter les données de classement. Notre analyse repose fortement sur deux astuces principales. La première est l’utilisation poussée de la distance du tau de Kendall, qui décompose les classements en comparaisons par paires. Cela nous permet d'analyser les distributions sur les classements à travers leurs marginales par paires et à travers une hypothèse spécifique appelée transitivité, qui empêche les cycles dans les préférences de se produire. La seconde est l'utilisation des fonctions de représentation adaptées aux données de classements, envoyant ces dernières dans un espace vectoriel. Trois problèmes différents, non supervisés et supervisés, ont été abordés dans ce contexte: l'agrégation de classement, la réduction de dimensionnalité et la prévision de classements avec variables explicatives.La première partie de cette thèse se concentre sur le problème de l'agrégation de classements, dont l'objectif est de résumer un ensemble de données de classement par un classement consensus. Parmi les méthodes existantes pour ce problème, la méthode d'agrégation de Kemeny se démarque. Ses solutions vérifient de nombreuses propriétés souhaitables, mais peuvent être NP-difficiles à calculer. Dans cette thèse, nous avons étudié la complexité de ce problème de deux manières. Premièrement, nous avons proposé une méthode pour borner la distance du tau de Kendall entre tout candidat pour le consensus (généralement le résultat d'une procédure efficace) et un consensus de Kemeny, sur tout ensemble de données. Nous avons ensuite inscrit le problème d'agrégation de classements dans un cadre statistique rigoureux en le reformulant en termes de distributions sur les classements, et en évaluant la capacité de généralisation de consensus de Kemeny empiriques.La deuxième partie de cette théorie est consacrée à des problèmes d'apprentissage automatique, qui se révèlent être étroitement liés à l'agrégation de classement. Le premier est la réduction de la dimensionnalité pour les données de classement, pour lequel nous proposons une approche de transport optimal, pour approximer une distribution sur les classements par une distribution montrant un certain type de parcimonie. Le second est le problème de la prévision des classements avec variables explicatives, pour lesquelles nous avons étudié plusieurs méthodes. Notre première proposition est d’adapter des méthodes constantes par morceaux à ce problème, qui partitionnent l'espace des variables explicatives en régions et assignent à chaque région un label (un consensus). Notre deuxième proposition est une approche de prédiction structurée, reposant sur des fonctions de représentations, aux avantages théoriques et computationnels, pour les données de classements
Ranking data, i.e., ordered list of items, naturally appears in a wide variety of situations, especially when the data comes from human activities (ballots in political elections, survey answers, competition results) or in modern applications of data processing (search engines, recommendation systems). The design of machine-learning algorithms, tailored for these data, is thus crucial. However, due to the absence of any vectorial structure of the space of rankings, and its explosive cardinality when the number of items increases, most of the classical methods from statistics and multivariate analysis cannot be applied in a direct manner. Hence, a vast majority of the literature rely on parametric models. In this thesis, we propose a non-parametric theory and methods for ranking data. Our analysis heavily relies on two main tricks. The first one is the extensive use of the Kendall’s tau distance, which decomposes rankings into pairwise comparisons. This enables us to analyze distributions over rankings through their pairwise marginals and through a specific assumption called transitivity, which prevents cycles in the preferences from happening. The second one is the extensive use of embeddings tailored to ranking data, mapping rankings to a vector space. Three different problems, unsupervised and supervised, have been addressed in this context: ranking aggregation, dimensionality reduction and predicting rankings with features.The first part of this thesis focuses on the ranking aggregation problem, where the goal is to summarize a dataset of rankings by a consensus ranking. Among the many ways to state this problem stands out the Kemeny aggregation method, whose solutions have been shown to satisfy many desirable properties, but can be NP-hard to compute. In this work, we have investigated the hardness of this problem in two ways. Firstly, we proposed a method to upper bound the Kendall’s tau distance between any consensus candidate (typically the output of a tractable procedure) and a Kemeny consensus, on any dataset. Then, we have casted the ranking aggregation problem in a rigorous statistical framework, reformulating it in terms of ranking distributions, and assessed the generalization ability of empirical Kemeny consensus.The second part of this thesis is dedicated to machine learning problems which are shown to be closely related to ranking aggregation. The first one is dimensionality reduction for ranking data, for which we propose a mass-transportation approach to approximate any distribution on rankings by a distribution exhibiting a specific type of sparsity. The second one is the problem of predicting rankings with features, for which we investigated several methods. Our first proposal is to adapt piecewise constant methods to this problem, partitioning the feature space into regions and locally assigning as final label (a consensus ranking) to each region. Our second proposal is a structured prediction approach, relying on embedding maps for ranking data enjoying theoretical and computational advantages
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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
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 importante para si. Por isso, a estrutura de hiperlink da WWW é conhecida por melhorar significativamente o desempenho das pesquisas para além do uso de estatísticas de distribuição simples de texto. Nesse sentido, a abordagem Hyperlink Induced Topic Search (HITS) introduz duas categorias básicas de páginas Web, hubs e autoridades, que revelam algumas informações semânticas ocultas a partir da estrutura de hiperlink. Em 2005, fizemos uma primeira extensão do HITS, denominada de Extended Hyperlink Induced Topic Search (XHITS), que inseriu duas novas categorias de páginas Web, quais sejam, novidades e portais. Na presente tese, revisamos o XHITS, transformando-o em uma generalização do HITS, ampliando o modelo de duas categorias para várias e apresentando um algoritmo eficiente de aprendizagem de máquina para calibrar o modelo proposto valendo-se de múltiplas categorias latentes. As descobertas aqui expostas indicam que a nova abordagem de aprendizagem fornece um modelo XHITS mais preciso. É importante registrar, por fim, que os experimentos realizados com a coleção ClueWeb09 25TB de páginas da WWW, baixadas em 2009, mostram que o XHITS pode melhorar significativamente a eficácia da pesquisa Web e produzir resultados comparáveis aos do TREC 2009/2010 Web Track, colocando-o na sexta posição, conforme os resultados publicados.
The rapid growth and generalized accessibility of the World Wide Web (WWW) have led to an increase in research in the field of the information retrieval for Web pages. The WWW is an immense and prodigious environment in which Web pages resemble a huge community of elements. These elements are connected via hyperlinks on the basis of similarity between the content of the pages, the popularity of a given page, the extent to which the information provided is authoritative in relation to a given field etc. In fact, when the author of a Web page links it to another, s/he is acknowledging the importance of the linked page to his/her information. As such the hyperlink structure of the WWW significantly improves research performance beyond the use of simple text distribution statistics. To this effect, the HITS approach introduces two basic categories of Web pages, hubs and authorities which uncover certain hidden semantic information using the hyperlink structure. In 2005, we made a first extension of HITS, called Extended Hyperlink Induced Topic Search (XHITS), which inserted two new categories of Web pages, which are novelties and portals. In this thesis, we revised the XHITS, transforming it into a generalization of HITS, broadening the model from two categories to various and presenting an efficient machine learning algorithm to calibrate the proposed model using multiple latent categories. The findings we set out here indicate that the new learning approach provides a more precise XHITS model. It is important to note, in closing, that experiments with the ClueWeb09 25TB collection of Web pages, downloaded in 2009, demonstrated that the XHITS is capable of significantly improving Web research efficiency and producing results comparable to those of the TREC 2009/2010 Web Track.
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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 graphique, ont porté les récentes avancées en vision par ordinateur. Ces avancées ont permis une amélioration drastique des performances des méthodes conventionnelles en biométrie, ce qui a accéléré leur adoption pour des usages concrets, et a provoqué un débat public sur l’utilisation de ces techniques. Dans ce contexte, les concepteurs de systèmes biométriques sont confrontés à un grand nombre de challenges dans l’apprentissage de ces réseaux. Dans cette thèse, nous considérons ces challenges du point de vue de l’apprentissage statistique théorique, ce qui nous amène à proposer ou esquisser des solutions concrètes. Premièrement, nous répondons à une prolifération de travaux sur l’apprentissage de similarité pour les réseaux profonds, qui optimisent des fonctions objectif détachées du but naturel d’ordonnancement recherché en biométrie. Précisément, nous introduisons la notion d’ordonnancement par similarité, en mettant en évidence la relation entre l’ordonnancement bipartite et la recherche d’une similarité adaptée à l’identification biométrique. Nous étendons ensuite la théorie sur l’ordonnancement bipartite à ce nouveau problème, tout en l’adaptant aux spécificités de l’apprentissage sur paires, notamment concernant son coût computationnel. Les fonctions objectif usuelles permettent d’optimiser la performance prédictive, mais de récents travaux ont mis en évidence la nécessité de prendre en compte d’autres facteurs lors de l’entraı̂nement d’un système biométrique, comme les biais présents dans les données, la robustesse des prédictions ou encore des questions d’équité. La thèse aborde ces trois exemples, en propose une étude statistique minutieuse, ainsi que des méthodes pratiques qui donnent les outils nécessaires aux concepteurs de systèmes biométriques pour adresser ces problématiques, sans compromettre la performance de leurs algorithmes
The rapid growth in population, combined with the increased mobility of people has created a need for sophisticated identity management systems.For this purpose, biometrics refers to the identification of individuals using behavioral or biological characteristics. The most popular approaches, i.e. fingerprint, iris or face recognition, are all based on computer vision methods. The adoption of deep convolutional networks, enabled by general purpose computing on graphics processing units, made the recent advances incomputer vision possible. These advances have led to drastic improvements for conventional biometric methods, which boosted their adoption in practical settings, and stirred up public debate about these technologies. In this respect, biometric systems providers face many challenges when learning those networks.In this thesis, we consider those challenges from the angle of statistical learning theory, which leads us to propose or sketch practical solutions. First, we answer to the proliferation of papers on similarity learningfor deep neural networks that optimize objective functions that are disconnected with the natural ranking aim sought out in biometrics. Precisely, we introduce the notion of similarity ranking, by highlighting the relationship between bipartite ranking and the requirements for similarities that are well suited to biometric identification. We then extend the theory of bipartite ranking to this new problem, by adapting it to the specificities of pairwise learning, particularly those regarding its computational cost. Usual objective functions optimize for predictive performance, but recentwork has underlined the necessity to consider other aspects when training a biometric system, such as dataset bias, prediction robustness or notions of fairness. The thesis tackles all of those three examplesby proposing their careful statistical analysis, as well as practical methods that provide the necessary tools to biometric systems manufacturers to address those issues, without jeopardizing the performance of their algorithms
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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.
Includes bibliographical references (leaves 119-127).
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 special case of semi-supervised learning. We examine how to incorporate instances from related tasks, with the appropriate penalty in the loss function to optimize performance on the hold out sets. We present a regularized manifold approach that allows us to learn a distance metric for the different instances directly from the data. This approach allows incorporation of information from related task examples, without prior estimation of cross-task coefficient covariances. We also present applications of ranking problems in two text analysis problems: a) Supervise content-word learning, and b) Company Entity matching for record linkage problems.
by Giorgos Zacharia.
Ph.D.
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Guo, Li Li. "Direct Optimization of Ranking Measures for Learning to Rank Models." Wright State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=wright1341520987.

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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. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98666-1.

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Learning to rank for information retrieval and natural language processing. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2011.

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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. Washington, D.C.]: 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. Washington, D.C: The Office, 1997.

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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 discussion emphasizes the challenges posed by these various subproblems, the insights and differences of the proposed solutions, and the outstanding questions that require further research.
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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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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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Huber, Franz. Belief and Counterfactuals. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780199976119.001.0001.

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This book is the first of two volumes on belief and counterfactuals. It consists of six of a total of eleven chapters. The first volume is concerned primarily with questions in epistemology and is expository in parts. Among other theories, it provides an accessible introduction to belief revision and ranking theory. Ranking theory specifies how conditional beliefs should behave. It does not tell us why they should do so nor what they are. This book fills these two gaps. The consistency argument tells us why conditional beliefs should obey the laws of ranking theory by showing them to be the means to attaining the end of holding true and informative beliefs. The conditional theory of conditional belief tells us what conditional beliefs are by specifying their nature in terms of non-conditional belief and counterfactuals. In addition, the book contains several novel arguments, accounts, and applications. These include an argument for the thesis that there are only hypothetical imperatives and no categorical imperatives; an account of the instrumentalist understanding of normativity, or rationality, according to which one ought to take the means to one’s ends; as well as solutions to the problems of conceptual belief change, logical learning, and learning conditionals. A distinctive feature of the book is its unifying methodological approach: means-end philosophy. Means-end philosophy takes serious that philosophy is a normative discipline, and that philosophical problems are entangled with each other. It also explains the importance of logic to philosophy, without being a technical theory itself.
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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. Washington, D.C: 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. Washington, D.C: The Office, 1997.

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Book chapters on the topic "Ranking learning"

1

Joshi, Ameet V. "Ranking." In Machine Learning and Artificial Intelligence, 193–98. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26622-6_20.

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Cossock, David, and Tong Zhang. "Subset Ranking Using Regression." In Learning Theory, 605–19. Berlin, Heidelberg: 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, 103–11. Berlin, Heidelberg: 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, 127–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-14267-3_9.

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Agarwal, Shivani, and Dan Roth. "Learnability of Bipartite Ranking Functions." In Learning Theory, 16–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11503415_2.

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Kamishima, Toshihiro, and Shotaro Akaho. "Dimension Reduction for Object Ranking." In Preference Learning, 203–15. Berlin, Heidelberg: 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, 45–64. Berlin, Heidelberg: 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, 155–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14125-6_8.

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Rendle, Steffen. "Learning Context-Aware Ranking." In Context-Aware Ranking with Factorization Models, 39–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16898-7_4.

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Grady, Leo J., and Jonathan R. Polimeni. "Manifold Learning and Ranking." In Discrete Calculus, 243–66. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-290-2_7.

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Conference papers on the topic "Ranking learning"

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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. New York, NY, USA: 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. New York, New York, USA: 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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Caragiannis, Ioannis, and Evi Micha. "Learning a Ground Truth Ranking Using Noisy Approval Votes." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/22.

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We consider a voting scenario where agents have opinions that are estimates of an underlying common ground truth ranking of the available alternatives, and each agent is asked to approve a set with her most preferred alternatives. We assume that estimates are implicitly formed using the well-known Mallows model for generating random rankings. We show that k-approval voting --- where all agents are asked to approve the same number k of alternatives and the outcome is obtained by sorting the alternatives in terms of their number of approvals --- has exponential sample complexity for all values of k. This negative result suggests that an exponential (in terms of the number of alternatives m) number of agents is always necessary in order to recover the ground truth ranking with high probability. In contrast, by just asking each agent to approve a random number of alternatives, the sample complexity improves dramatically: it now depends only polynomially on m. Our results may have implications on the effectiveness of crowdsourcing applications that ask workers to provide their input by approving sets of available alternatives.
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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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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. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2600428.2609619.

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Meng, Jiana, and Hongfei Lin. "Transfer learning based on graph ranking." In 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2012. http://dx.doi.org/10.1109/fskd.2012.6233765.

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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), April 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 composition by comparing two otherwise similar cohorts of students admitted before and after the policy change. Average grade 8 test scores across math and Indonesian declined by 0.08 s.d. (not significant). To understand which students throughout the education system gained and lost in terms of learning, we simulated public school access under the 2018 policy and its predecessor for both cohorts. In public schools, teachers attempted to adapt lessons to lower-scoring students by changing teaching approaches and tracking students. These responses and/or exposure to different peers negatively affected learning for students predicted to have access to public schools under both policies (-0.13 s.d., significant at the 10 percent level) and aided students with predicted public school access under the new policy slightly (0.12 s.d., not significant). These results are in contrast to existing literature which finds little or no impact from shifts in student composition on incumbent students’ learning. In private schools, we found no such adaptations and no effects on predicted incumbent students. However, students predicted to enter private schools under the new policy saw large negative effects (-0.24 s.d., significant), due to lower school quality and/or peer effects. Our results demonstrate that effects from high-performing, selective schools can be highly heterogenous and influenced by student composition.
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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), April 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 composition by comparing two otherwise similar cohorts of students admitted before and after the policy change. Average grade 8 test scores across math and Indonesian declined by 0.08 s.d. (not significant). To understand which students throughout the education system gained and lost in terms of learning, we simulated public school access under the 2018 policy and its predecessor for both cohorts. In public schools, teachers attempted to adapt lessons to lower-scoring students by changing teaching approaches and tracking students. These responses and/or exposure to different peers negatively affected learning for students predicted to have access to public schools under both policies (-0.13 s.d., significant at the 10 percent level) and aided students with predicted public school access under the new policy slightly (0.12 s.d., not significant). These results are in contrast to existing literature which finds little or no impact from shifts in student composition on incumbent students’ learning. In private schools, we found no such adaptations and no effects on predicted incumbent students. However, students predicted to enter private schools under the new policy saw large negative effects (-0.24 s.d., significant), due to lower school quality and/or peer effects. Our results demonstrate that effects from high-performing, selective schools can be highly heterogenous and influenced by student composition.
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Sandford, Robert, Vladimir Smakhtin, Colin Mayfield, Hamid Mehmood, John Pomeroy, Chris Debeer, Phani Adapa, et al. Canada in the Global Water World: Analysis of Capabilities. United Nations University Institute for Water, Environment and Health, November 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 subject areas important in water. In most cases, however, this has not led to the establishment of specific water study programmes. Only a few universities provide integrated water education. There is a need for a comprehensive listing of water-related educational activities in universities and colleges — a useful resource for potential students and employers. A review of recent Canadian water research directions and highlights reveals strong and diverse water research capacity and placed the country among global leaders in this field. Canada appears to be within the top 10 countries in terms of water research productivity (publications) and research impact (citations). Research capacity has been traditionally strong in the restoration and protection of the lakes, prediction of changes in climate, water and cryosphere (areas where water is in solid forms such as ice and snow), prediction and management of floods and droughts. There is also a range of other strong water research directions. Canada is not among the top 10 global water aid donors in absolute dollar numbers; the forerunners are, as a rule, the countries with higher GDP per capita. Canadian investments in Africa water development were consistently higher over the years than investments in other regions of the global South. The contributions dropped significantly in recent years overall, also with a decline in aid flow to Africa. Given government support for the right business model and access to resources, there is significant capacity within the Canadian water sector to deliver water technology projects with effective sustainable outcomes for the developing world. The report recommends several potential avenues to elevate Canada’s role on the global water stage, i.e. innovative, diverse and specific approaches such as developing a national inventory of available water professional capacity, and ranking Universities on the strength of their water programmes coordinating national contributions to global sustainability processes around the largest ever university-led water research programme in the world – the 7-year Global Water Futures program targeting specific developmental or regional challenges through overseas development aid to achieve quick wins that may require only modest investments resolving such chronic internal water challenges as water supply and sanitation of First Nations, and illustrating how this can be achieved within a limited period with good will strengthening and expanding links with UN-Water and other UN organisations involved in global water policy work To improve water management at home, and to promote water Canadian competence abroad, the diverse efforts of the country’s water sector need better coordination. There is a significant role for government at all levels, but especially federally, in this process.
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