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Dissertations / Theses on the topic 'Ranking learning'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 July 2025

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1

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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2

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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3

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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4

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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5

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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7

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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9

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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10

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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11

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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12

Ross, Jacob W. "Features for Ranking Tweets Based on Credibility and Newsworthiness." Wright State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1431037057.

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13

Lyubchyk, Leonid, Oleksy Galuza, and Galina Grinberg. "Ranking Model Real-Time Adaptation via Preference Learning Based on Dynamic Clustering." Thesis, ННК "IПСА" НТУУ "КПI iм. Iгоря Сiкорського", 2017. http://repository.kpi.kharkov.ua/handle/KhPI-Press/36819.

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The proposed preference learning on clusters method allows to fully realizing the advantages of the kernel-based approach. While the dimension of the model is determined by a pre-selected number of clusters and its complexity do not grow with increasing number of observations. Thus real-time preference function identification algorithm based on training data stream includes successive estimates of cluster parameter as well as average cluster ranks updating and recurrent kernel-based nonparametric estimation of preference model.

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Albakour, Subhy. "Stream-automl : automated machine learning overimbalanced data streams for bipartite ranking problems." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAT015.

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Malgré sa popularité dans la littérature scientifique, l’apprentissage en ligne doit encore concrétiser son utilité pratique dans les applications industrielles. Vu que l’apprentissage en ligne gère les flux incessants de données volumineuses, à haute vélocité et en évolution constante par conception, le marketing en ligne semble être le candidat favori pour que l’apprentissage en ligne fasse son entrée dans l’industrie. Dans ce contexte, l’état de l’art de l’apprentissage en ligne n’a qu’une utilité limitée, car il se concentre principalement sur les problèmes de classification, tandis que le

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15

Mohotti, Wathsala Anupama. "Unsupervised text mining: Effective similarity calculation with ranking and matrix factorization." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/199456/1/Wathsala_Mohotti_Thesis.pdf.

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The popularity of the internet has increased the availability of digital text on social media, online forums, news broadcasting services, web blogs, and websites. Text mining is an effective approach to extract concepts, clusters, user communities, outliers and dynamic changes in the text collections. This thesis developed unsupervised machine learning techniques to identify these patterns by dealing with high dimensional and sparse text data. More specifically, this thesis developed effective measures of pairwise similarity and extended them in implementing clustering, outlier detection and c

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16

Matsubara, Edson Takashi. "Relações entre ranking, análise ROC e calibração em aprendizado de máquina." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-04032009-114050/.

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Aprendizado supervisionado tem sido principalmente utilizado para classificação. Neste trabalho são mostrados os benefícios do uso de rankings ao invés de classificação de exemplos isolados. Um rankeador é um algoritmo que ordena um conjunto de exemplos de tal modo que eles são apresentados do exemplo de maior para o exemplo de menor expectativa de ser positivo. Um ranking é o resultado dessa ordenação. Normalmente, um ranking é obtido pela ordenação do valor de confiança de classificação dado por um classificador. Este trabalho tem como objetivo procurar por novas abordagens para promover o u

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Ibstedt, Julia, Elsa Rådahl, Erik Turesson, and Voorde Magdalena vande. "Application and Further Development of TrueSkill™ Ranking in Sports." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-384863.

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The aim of this study was to explore the ranking model TrueSkill™ developed by Microsoft, applying it on various sports and constructing extensions to the model. Two different inference methods for TrueSkill was constructed using Gibbs sampling and message passing. Additionally, the sequential method using Gibbs sampling was successfully extended into a batch method, in order to eliminate game order dependency and creating a fairer, although computationally heavier, ranking system. All methods were further implemented with extensions for taking home team advantage, score difference and finally

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18

Ataman, Kaan. "Learning to rank by maximizing the AUC with linear programming for problems with binary output." Diss., University of Iowa, 2007. http://ir.uiowa.edu/etd/151.

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Puthiya, Parambath Shameem Ahamed. "New methods for multi-objective learning." Thesis, Compiègne, 2016. http://www.theses.fr/2016COMP2322/document.

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Les problèmes multi-objectifs se posent dans plusieurs scénarios réels dans le monde où on doit trouver une solution optimale qui soit un compromis entre les différents objectifs en compétition. Dans cette thèse, on étudie et on propose des algorithmes pour traiter les problèmes des machines d’apprentissage multi-objectif. On étudie deux méthodes d’apprentissage multi-objectif en détail. Dans la première méthode, on étudie le problème de trouver le classifieur optimal pour réaliser des mesures de performances multivariées. Dans la seconde méthode, on étudie le problème de classer des informati

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20

Stojkovic, Ivan. "Functional Norm Regularization for Margin-Based Ranking on Temporal Data." Diss., Temple University Libraries, 2018. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/522550.

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Computer and Information Science<br>Ph.D.<br>Quantifying the properties of interest is an important problem in many domains, e.g., assessing the condition of a patient, estimating the risk of an investment or relevance of the search result. However, the properties of interest are often latent and hard to assess directly, making it difficult to obtain classification or regression labels, which are needed to learn a predictive models from observable features. In such cases, it is typically much easier to obtain relative comparison of two instances, i.e. to assess which one is more intense (with

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Alshehri, Adel. "A Machine Learning Approach to Predicting Community Engagement on Social Media During Disasters." Scholar Commons, 2019. https://scholarcommons.usf.edu/etd/7728.

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The use of social media is expanding significantly and can serve a variety of purposes. Over the last few years, users of social media have played an increasing role in the dissemination of emergency and disaster information. It is becoming more common for affected populations and other stakeholders to turn to Twitter to gather information about a crisis when decisions need to be made, and action is taken. However, social media platforms, especially on Twitter, presents some drawbacks when it comes to gathering information during disasters. These drawbacks include information overload, message

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22

Salomon, Sophie. "Bias Mitigation Techniques and a Cost-Aware Framework for Boosted Ranking Algorithms." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1586450345426827.

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23

Reed, Jeremy T. "Acoustic segment modeling and preference ranking for music information retrieval." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37189.

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This dissertation focuses on improving content-based recommendation systems for music. Specifically, progress in the development in music content-based recommendation systems has stalled in recent years due to some faulty assumptions: 1. most acoustic content-based systems for music information retrieval (MIR) assume a bag-of-frames model, where it is assumed that a song contains a simplistic, global audio texture 2. genre, style, mood, and authors are appropriate categories for machine-oriented recommendation 3. similarity is a universal construct and does not vary among different users The

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24

Kim, Jinhan. "J-model : an open and social ensemble learning architecture for classification." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/7672.

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Ensemble learning is a promising direction of research in machine learning, in which an ensemble classifier gives better predictive and more robust performance for classification problems by combining other learners. Meanwhile agent-based systems provide frameworks to share knowledge from multiple agents in an open context. This thesis combines multi-agent knowledge sharing with ensemble methods to produce a new style of learning system for open environments. We now are surrounded by many smart objects such as wireless sensors, ambient communication devices, mobile medical devices and even inf

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Dokania, Puneet Kumar. "High-Order Inference, Ranking, and Regularization Path for Structured SVM." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLC044/document.

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Cette thèse présente de nouvelles méthodes pour l'application de la prédiction structurée en vision numérique et en imagerie médicale.Nos nouvelles contributions suivent quatre axes majeurs.La première partie de cette thèse étudie le problème d'inférence d'ordre supérieur.Nous présentons une nouvelle famille de problèmes de minimisation d'énergie discrète, l'étiquetage parcimonieux, encourageant la parcimonie des étiquettes.C'est une extension naturelle des problèmes connus d'étiquetage de métriques aux potentiels d'ordre élevé.Nous proposons par ailleurs une généralisation du modèle Pn-Potts,

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Lipscombe, Trevor, and n/a. "Different teachers for different students? : The relationship between learning style, other student variables and students' ranking of teacher characteristics." University of Canberra. Education, 1989. http://erl.canberra.edu.au./public/adt-AUC20060817.141319.

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This study examined the influence of selected student variables (learning style , age, sex, nationality (birthplace), academic achievement, and social class) on the ranking of twelve teacher characteristics. 246 ACT TAFE Associate Diploma in Business students formed the sample. Results were compared with a similar study by Travis (1987) of secondary students in Canada and USA. The extent to which different groups of students prefer different teacher characteristics has important implications for the growing practice of student rating of teachers' effectiveness. This practice (operating under a

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Noor, Ifada. "Personalized ranking for tag-based item recommendation system using tensor model." Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/98426/1/_Noor%20Ifada_Thesis.pdf.

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This research is a step forward in the study of generating item recommendations for the tag-based systems, in which two efficient tagging data interpretation schemes and four ranking methods are developed. The interpretation schemes apply ranking constraints to interpret the tagging data that allow a ranked representation and result in richer data. The ranking methods fall into the category of point-wise and list-wise based ranking approaches that consider the recommendation task as regression/classification and ranking respectively. This thesis, in particular, shows that tagging data interpre

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Jiao, Jian. "A framework for finding and summarizing product defects, and ranking helpful threads from online customer forums through machine learning." Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/23159.

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The Internet has revolutionized the way users share and acquire knowledge. As important and popular Web-based applications, online discussion forums provide interactive platforms for users to exchange information and report problems. With the rapid growth of social networks and an ever increasing number of Internet users, online forums have accumulated a huge amount of valuable user-generated data and have accordingly become a major information source for business intelligence. This study focuses specifically on product defects, which are one of the central concerns of manufacturing companies

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Safran, Mejdl Sultan. "EFFICIENT LEARNING-BASED RECOMMENDATION ALGORITHMS FOR TOP-N TASKS AND TOP-N WORKERS IN LARGE-SCALE CROWDSOURCING SYSTEMS." OpenSIUC, 2018. https://opensiuc.lib.siu.edu/dissertations/1511.

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A pressing need for efficient personalized recommendations has emerged in crowdsourcing systems. On the one hand, workers confront a flood of tasks, and they often spend too much time to find tasks matching their skills and interests. Thus, workers want effective recommendation of the most suitable tasks with regard to their skills and preferences. On the other hand, requesters sometimes receive results in low-quality completion since a less qualified worker may start working on a task before a better-skilled worker may get hands on. Thus, requesters want reliable recommendation of the best wo

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Harrington, Edward, and edwardharrington@homemail com au. "Aspects of Online Learning." The Australian National University. Research School of Information Sciences and Engineering, 2004. http://thesis.anu.edu.au./public/adt-ANU20060328.160810.

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Online learning algorithms have several key advantages compared to their batch learning algorithm counterparts: they are generally more memory efficient, and computationally mor efficient; they are simpler to implement; and they are able to adapt to changes where the learning model is time varying. Online algorithms because of their simplicity are very appealing to practitioners. his thesis investigates several online learning algorithms and their application. The thesis has an underlying theme of the idea of combining several simple algorithms to give better performance. In this thesis we inv

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Paris, Bruno Mendonça. "Learning to rank: combinação de algoritmos aplicando stacking e análise dos resultados." Universidade Presbiteriana Mackenzie, 2017. http://tede.mackenzie.br/jspui/handle/tede/3494.

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Zhang, Ganqin. "Bipartite RankBoost+: An Improvement to Bipartite RankBoost." Case Western Reserve University School of Graduate Studies / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case160767885657324.

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Arvidsson, Simon, and Marcus Gullstrand. "Predicting forest strata from point clouds using geometric deep learning." Thesis, Jönköping University, JTH, Avdelningen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-54155.

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Introduction: Number of strata (NoS) is an informative descriptor of forest structure and is therefore useful in forest management. Collection of NoS as well as other forest properties is performed by fieldworkers and could benefit from automation. Objectives: This study investigates automated prediction of NoS from airborne laser scanned point clouds over Swedish forest plots.Methods: A previously suggested approach of using vertical gap probability is compared through experimentation against the geometric neural network PointNet++ configured for ordinal prediction. For both approaches, the m

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Mittal, Arpit. "Human layout estimation using structured output learning." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:bb290cfd-5216-42d7-b3d2-c2b4b01614bc.

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In this thesis, we investigate the problem of human layout estimation in unconstrained still images. This involves predicting the spatial configuration of body parts. We start our investigation with pictorial structure models and propose an efficient method of model fitting using skin regions. To detect the skin, we learn a colour model locally from the image by detecting the facial region. The resulting skin detections are also used for hand localisation. Our next contribution is a comprehensive dataset of 2D hand images. We collected this dataset from publicly available image sources, and an

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Lin, Xiao. "Leveraging Multimodal Perspectives to Learn Common Sense for Vision and Language Tasks." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/79521.

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Learning and reasoning with common sense is a challenging problem in Artificial Intelligence (AI). Humans have the remarkable ability to interpret images and text from different perspectives in multiple modalities, and to use large amounts of commonsense knowledge while performing visual or textual tasks. Inspired by that ability, we approach commonsense learning as leveraging perspectives from multiple modalities for images and text in the context of vision and language tasks. Given a target task (e.g., textual reasoning, matching images with captions), our system first represents input imag

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Bjöörn, Anton. "Employing a Transformer Language Model for Information Retrieval and Document Classification : Using OpenAI's generative pre-trained transformer, GPT-2." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281766.

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As the information flow on the Internet keeps growing it becomes increasingly easy to miss important news which does not have a mass appeal. Combating this problem calls for increasingly sophisticated information retrieval methods. Pre-trained transformer based language models have shown great generalization performance on many natural language processing tasks. This work investigates how well such a language model, Open AI’s General Pre-trained Transformer 2 model (GPT-2), generalizes to information retrieval and classification of online news articles, written in English, with the purpose of

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Chen, Xi. "Learning with Sparcity: Structures, Optimization and Applications." Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/228.

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The development of modern information technology has enabled collecting data of unprecedented size and complexity. Examples include web text data, microarray & proteomics, and data from scientific domains (e.g., meteorology). To learn from these high dimensional and complex data, traditional machine learning techniques often suffer from the curse of dimensionality and unaffordable computational cost. However, learning from large-scale high-dimensional data promises big payoffs in text mining, gene analysis, and numerous other consequential tasks. Recently developed sparse learning techniques p

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Blumm, Nicolas C. "On the Purpose & Ethics of Elite Higher Education." Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/cmc_theses/1713.

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This thesis explores the fundamental ethics and purpose of elite higher education. Beginning with an inquiry into the history of American higher education, this work reveals that the U.S. News & World Report “Best College” and “Best University” ranking lists hold an increasingly important role in distinguishing institutions, particularly those within the elite tier. Following an examination of the U.S. News’ methodology, this analysis confronts concerns with individual access to elite institutions. Although there are potential changes to the U.S. News’ methodology that could improve institutio

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Lee, Joonseok. "Local approaches for collaborative filtering." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53846.

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Recommendation systems are emerging as an important business application as the demand for personalized services in E-commerce increases. Collaborative filtering techniques are widely used for predicting a user's preference or generating a list of items to be recommended. In this thesis, we develop several new approaches for collaborative filtering based on model combination and kernel smoothing. Specifically, we start with an experimental study that compares a wide variety of CF methods under different conditions. Based on this study, we formulate a combination model similar to boosting but w

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Ben, Qingyan. "Flight Sorting Algorithm Based on Users’ Behaviour." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-294132.

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The model predicts the best flight order and recommend best flight to users. The thesis could be divided into the following three parts: Feature choosing, data-preprocessing, and various algorithms experiment. For feature choosing, besides the original information of flight itself, we add the user’s selection status into our model, which the flight class is, together with children or not. In the data preprocessing stage, data cleaning is used to process incomplete and repeated data. Then a normalization method removes the noise in the data. After various balancing processing, the class-imbalan

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Zeng, Kaiman. "Next Generation of Product Search and Discovery." FIU Digital Commons, 2015. http://digitalcommons.fiu.edu/etd/2312.

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Online shopping has become an important part of people’s daily life with the rapid development of e-commerce. In some domains such as books, electronics, and CD/DVDs, online shopping has surpassed or even replaced the traditional shopping method. Compared with traditional retailing, e-commerce is information intensive. One of the key factors to succeed in e-business is how to facilitate the consumers’ approaches to discover a product. Conventionally a product search engine based on a keyword search or category browser is provided to help users find the product information they need. The genera

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Alaya, Mili Nourhene. "Managing the empirical hardness of the ontology reasoning using the predictive modelling." Thesis, Paris 8, 2016. http://www.theses.fr/2016PA080062/document.

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Multiples techniques d'optimisation ont été implémentées afin de surmonter le compromis entre la complexité des algorithmes du raisonnement et l'expressivité du langage de formulation des ontologies. Cependant les compagnes d'évaluation des raisonneurs continuent de confirmer l'aspect imprévisible et aléatoire des performances de ces logiciels à l'égard des ontologies issues du monde réel. Partant de ces observations, l'objectif principal de cette thèse est d'assurer une meilleure compréhension du comportement empirique des raisonneurs en fouillant davantage le contenu des ontologies. Nous avo

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Clémençon, Stéphan. "Résumé des Travaux en Statistique et Applications des Statistiques." Habilitation à diriger des recherches, Université de Nanterre - Paris X, 2006. http://tel.archives-ouvertes.fr/tel-00138299.

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Ce rapport présente brièvement l'essentiel de mon activité de recherche depuis ma thèse de doctorat [53], laquelle visait principalement à étendre l'utilisation des progrès récents de l'Analyse Harmonique Algorithmique pour l'estimation non paramétrique adaptative dans le cadre d'observations i.i.d. (tels que l'analyse par ondelettes) à l'estimation statistique pour des données markoviennes. Ainsi qu'il est éxpliqué dans [123], des résultats relatifs aux propriétés de concentration de la mesure (i.e. des inégalités de probabilité et de moments sur certaines classes fonctionnelles, adaptées à l

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Peel, Thomas. "Algorithmes de poursuite stochastiques et inégalités de concentration empiriques pour l'apprentissage statistique." Thesis, Aix-Marseille, 2013. http://www.theses.fr/2013AIXM4769/document.

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La première partie de cette thèse introduit de nouveaux algorithmes de décomposition parcimonieuse de signaux. Basés sur Matching Pursuit (MP) ils répondent au problème suivant : comment réduire le temps de calcul de l'étape de sélection de MP, souvent très coûteuse. En réponse, nous sous-échantillonnons le dictionnaire à chaque itération, en lignes et en colonnes. Nous montrons que cette approche fondée théoriquement affiche de bons résultats en pratique. Nous proposons ensuite un algorithme itératif de descente de gradient par blocs de coordonnées pour sélectionner des caractéristiques en cl

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Ouni, Zaïd. "Statistique pour l’anticipation des niveaux de sécurité secondaire des générations de véhicules." Thesis, Paris 10, 2016. http://www.theses.fr/2016PA100099/document.

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La sécurité routière est une priorité mondiale, européenne et française. Parce que les véhicules légers (ou simplement “les véhicules”) sont évidemment l’un des acteurs principaux de l’activité routière, l'amélioration de la sécurité routière passe nécessairement par l’analyse de leurs caractéristiques accidentologiques. Si les nouveaux véhicules sont développés en bureau d’étude et validés en laboratoire, c’est la réalité accidentologique qui permet de vraiment cerner comment ils se comportent en matière de sécurité secondaire, c’est-à-dire quelle sécurité ils offrent à leurs occupants lors d

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Лозова, К. А. "Оцінювання якості професорсько-викладацького складу ВНЗ у дистанційній освіті". Thesis, Сумський державний університет, 2015. http://essuir.sumdu.edu.ua/handle/123456789/39684.

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Перехід від класичних форм навчання до більш конкурентним формам дистанційного навчання базується на змінах як в організації учбового процесу, так і в підходах що до визначення якості освіти. Відбувається зміна пріоритетності показників в рейтинговій оцінці університетів, факультетів та викладачів.

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Mehta, Khushang Samir. "Using Machine Learning for Incremental Aggregation of Collaborative Rankings." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613745957050039.

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Guan, Wei. "New support vector machine formulations and algorithms with application to biomedical data analysis." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/41126.

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The Support Vector Machine (SVM) classifier seeks to find the separating hyperplane wx=r that maximizes the margin distance 1/||w||2^2. It can be formalized as an optimization problem that minimizes the hinge loss Ʃ[subscript i](1-y[subscript i] f(x[subscript i]))₊ plus the L₂-norm of the weight vector. SVM is now a mainstay method of machine learning. The goal of this dissertation work is to solve different biomedical data analysis problems efficiently using extensions of SVM, in which we augment the standard SVM formulation based on the application requirements. The biomedical applications

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Depecker, Marine. "Méthodes d'apprentissage statistique pour le scoring." Phd thesis, Paris, Télécom ParisTech, 2010. https://pastel.hal.science/pastel-00572421.

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Cette thèse porte sur le développement d'une méthode non-paramétrique pour l'apprentissage supervisé de règles d'ordonnancement à partir de données étiquetées de façon binaire. Cette méthode repose sur le partitionnement récursif de l'espace des observations et généralise la notion d'arbre de décision au problème de l'ordonnancement, les règles de score produites pouvant être représentées graphiquement par des arbres binaires et orientés. Afin de proposer une méthode d'apprentissage flexible, nous introduisons une procédure permettant, à chaque itération de l'algorithme, de scinder l'espace de

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Depecker, Marine. "Méthodes d'apprentissage statistique pour le scoring." Phd thesis, Télécom ParisTech, 2010. http://pastel.archives-ouvertes.fr/pastel-00572421.

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Cette thèse porte sur le développement d'une méthode non-paramétrique pour l'apprentissage supervisé de règles d'ordonnancement à partir de données étiquetées de façon binaire. Cette méthode repose sur le partitionnement récursif de l'espace des observations et généralise la notion d'arbre de décision au problème de l'ordonnancement, les règles de score produites pouvant être représentées graphiquement par des arbres binaires et orientés. Afin de proposer une méthode d'apprentissage flexible, nous introduisons une procédure permettant, à chaque itération de l'algorithme, de scinder l'espace de

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