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Tesis sobre el tema "Learning on graphs"

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Publicado: 10 de marzo de 2023
Última modificación: 11 de marzo de 2023

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1

Vitale, F. "FAST LEARNING ON GRAPHS." Doctoral thesis, Università degli Studi di Milano, 2011. http://hdl.handle.net/2434/155500.

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We carry out a systematic study of classification problems on networked data, presenting novel techniques with good performance both in theory and in practice. We assess the power of node classification based on class-linkage information only. In particular, we propose four new algorithms that exploit the homiphilic bias (linked entities tend to belong to the same class) in different ways. The set of the algorithms we present covers diverse practical needs: some of them operate in an active transductive setting and others in an on-line transductive setting. A third group works within
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2

Irniger, Christophe-André. "Graph matching filtering databases of graphs using machine learning techniques." Berlin Aka, 2005. http://deposit.ddb.de/cgi-bin/dokserv?id=2677754&prov=M&dok_var=1&dok_ext=htm.

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3

Simonovsky, Martin. "Deep learning on attributed graphs." Thesis, Paris Est, 2018. http://www.theses.fr/2018PESC1133/document.

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Le graphe est un concept puissant pour la représentation des relations entre des paires d'entités. Les données ayant une structure de graphes sous-jacente peuvent être trouvées dans de nombreuses disciplines, décrivant des composés chimiques, des surfaces des modèles tridimensionnels, des interactions sociales ou des bases de connaissance, pour n'en nommer que quelques-unes. L'apprentissage profond (DL) a accompli des avancées significatives dans une variété de tâches d'apprentissage automatique au cours des dernières années, particulièrement lorsque les données sont structurées sur une grille
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4

Rosar, Kós Lassance Carlos Eduardo. "Graphs for deep learning representations." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2020. http://www.theses.fr/2020IMTA0204.

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Ces dernières années, les méthodes d'apprentissage profond ont atteint l'état de l'art dans une vaste gamme de tâches d'apprentissage automatique, y compris la classification d'images et la traduction automatique. Ces architectures sont assemblées pour résoudre des tâches d'apprentissage automatique de bout en bout. Afin d'atteindre des performances de haut niveau, ces architectures nécessitent souvent d'un très grand nombre de paramètres. Les conséquences indésirables sont multiples, et pour y remédier, il est souhaitable de pouvoir comprendre ce qui se passe à l'intérieur des architectures d
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5

Ghiasnezhad, Omran Pouya. "Rule Learning in Knowledge Graphs." Thesis, Griffith University, 2018. http://hdl.handle.net/10072/382680.

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With recent advancements in knowledge extraction and knowledge management systems, an enormous number of knowledge bases have been constructed, such as YAGO, and Wikidata. These automatically built knowledge bases which contain millions of entities and their relations have been stored in graph-based schemas, and thus are usually referred to as knowledge graphs (KGs). Since KGs have been built based on the limited available data, they are far from complete. However, learning frequent patterns in the form of logical rules from these incomplete KGs has two main advantages. First, by applying the
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6

Fan, Shuangfei. "Deep Representation Learning on Labeled Graphs." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/96596.

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We introduce recurrent collective classification (RCC), a variant of ICA analogous to recurrent neural network prediction. RCC accommodates any differentiable local classifier and relational feature functions. We provide gradient-based strategies for optimizing over model parameters to more directly minimize the loss function. In our experiments, this direct loss minimization translates to improved accuracy and robustness on real network data. We demonstrate the robustness of RCC in settings where local classification is very noisy, settings that are particularly challenging for ICA. As a new
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7

Rommedahl, David, and Martin Lindström. "Learning Sparse Graphs for Data Prediction." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-295623.

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Graph structures can often be used to describecomplex data sets. In many applications, the graph structureis not known but must be inferred from data. Furthermore, realworld data is often naturally described by sparse graphs. Inthis project, we have aimed at recreating the results describedin previous work, namely to learn a graph that can be usedfor prediction using an ℓ1-penalised LASSO approach. We alsopropose different methods for learning and evaluating the graph. We have evaluated the methods on synthetic data and real-worldSwedish temperature data. The results show that we are unableto
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8

Xu, Keyulu. "Graph structures, random walks, and all that : learning graphs with jumping knowledge networks." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/121660.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 51-54).<br>Graph representation learning aims to extract high-level features from the graph structures and node features, in order to make predictions about the nodes and the graphs. Applications include predicting chemical prope
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9

Freeman, Guy. "Learning and predicting with chain event graphs." Thesis, University of Warwick, 2010. http://wrap.warwick.ac.uk/4529/.

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Graphical models provide a very promising avenue for making sense of large, complex datasets. The most popular graphical models in use at the moment are Bayesian networks (BNs). This thesis shows, however, they are not always ideal factorisations of a system. Instead, I advocate for the use of a relatively new graphical model, the chain event graph (CEG), that is based on event trees. Event trees directly represent graphically the event space of a system. Chain event graphs reduce their potentially huge dimensionality by taking into account identical probability distributions on some of the ev
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10

Pasteris, S. U. "Efficient algorithms for online learning over graphs." Thesis, University College London (University of London), 2016. http://discovery.ucl.ac.uk/1516210/.

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In this thesis we consider the problem of online learning with labelled graphs, in particular designing algorithms that can perform this problem quickly and with low memory requirements. We consider the tasks of Classification (in which we are asked to predict the labels of vertices) and Similarity Prediction (in which we are asked to predict whether two given vertices have the same label). The first half of the thesis considers non- probabilistic online learning, where there is no probability distribution on the labelling and we bound the number of mistakes of an algorithm by a function of th
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11

Sonntag, Dag. "Chain Graphs : Interpretations, Expressiveness and Learning Algorithms." Doctoral thesis, Linköpings universitet, Databas och informationsteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-125921.

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Probabilistic graphical models are currently one of the most commonly used architectures for modelling and reasoning with uncertainty. The most widely used subclass of these models is directed acyclic graphs, also known as Bayesian networks, which are used in a wide range of applications both in research and industry. Directed acyclic graphs do, however, have a major limitation, which is that only asymmetric relationships, namely cause and effect relationships, can be modelled between their variables. A class of probabilistic graphical models that tries to address this shortcoming is chain gra
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12

Eid, Abdelrahman. "Stochastic simulations for graphs and machine learning." Thesis, Lille 1, 2020. http://www.theses.fr/2020LIL1I018.

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Bien qu’il ne soit pas pratique d’étudier la population dans de nombreux domaines et applications, l’échantillonnage est une méthode nécessaire permettant d’inférer l’information.Cette thèse est consacrée au développement des algorithmes d’échantillonnage probabiliste pour déduire l’ensemble de la population lorsqu’elle est trop grande ou impossible à obtenir.Les techniques Monte Carlo par chaîne de markov (MCMC) sont l’un des outils les plus importants pour l’échantillonnage à partir de distributions de probabilités surtout lorsque ces distributions ont des constantes de normalisation diffici
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13

Li, Qilin. "Affinity Learning on Graphs with Diffusion Processes." Thesis, Curtin University, 2020. http://hdl.handle.net/20.500.11937/80408.

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In the thesis, we propose machine learning algorithms utilising diffusion processes to learn the pairwise affinity between data samples. Diffusion processes propagates neighbour information on a node-edge graph, resulting in context-aware affinities that is smooth to the data manifold structure. Similar ideas are also embedded in graph convolutional networks for representation learning. These proposed algorithms improve performance for various machine learning tasks, such as data cluster analysis, dimensionality reduction, and semisupervised classification.
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14

Nguyen, Thi Kim Hue. "Structure learning of graphs for count data." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3421952.

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Biological processes underlying the basic functions of a cell involve complex interactions between genes. From a technical point of view, these interactions can be represented through a graph where genes and their connections are, respectively, nodes and edges. The main research objective of this thesis is to develop a statistical framework for modelling the interactions between genes when the activity of genes is measured on a discrete scale. We propose several algorithms. First, we define an algorithm for learning the structure of a undirected graph, proving its theoretical consistence in
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15

Santacruz, Muñoz José Luis. "Error-tolerant Graph Matching on Huge Graphs and Learning Strategies on the Edit Costs." Doctoral thesis, Universitat Rovira i Virgili, 2019. http://hdl.handle.net/10803/668356.

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Els grafs són estructures de dades abstractes que s'utilitzen per a modelar problemes reals amb dues entitats bàsiques: nodes i arestes. Cada node o vèrtex representa un punt d'interès rellevant d'un problema, i cada aresta representa la relació entre aquests vèrtexs. Els nodes i les arestes podrien incorporar atributs per augmentar la precisió del problema modelat. Degut a aquesta versatilitat, s'han trobat moltes aplicacions en camps com la visió per computador, biomèdics, anàlisi de xarxes, etc. La Distància d'edició de grafs (GED) s'ha convertit en una eina important en el reconeixement de
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16

Ricatte, Thomas. "Hypernode graphs for learning from binary relations between sets of objects." Thesis, Lille 3, 2015. http://www.theses.fr/2015LIL30001/document.

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17

Amundsson, Karl. "Approximate Bayesian Learning of Partition Directed Acyclic Graphs." Thesis, KTH, Matematisk statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-192853.

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Partition directed acyclic graphs (PDAGs) is a model whereby the conditional probability tables (CPTs) are partitioned into parts with equal probability. In this way, the number of parameters that need to be learned can be significantly reduced so that some problems become more computationally feasible. PDAGs have been shown to be connected to labeled DAGs (LDAGs) and the connection is summarized here. Furthermore, a clustering algorithm is compared to an exact algorithm for determining a PDAG. To evaluate the algorithm, we use it on simulated data where the expected result is known.<br>Partit
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18

Ma, Zongjie. "Searching on Massive Graphs and Regularizing Deep Learning." Thesis, Griffith University, 2018. http://hdl.handle.net/10072/385875.

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We have designed di erent heuristics for both searching on Massive graphs and regularizing Deep Neural Networks in this work. Both the problem of nding a minimum vertex cover (MinVC) and the maximum edge weight clique (MEWC) in a graph are prominent NP-hard problems of great importance in both theory and application. During recent decades, there has been much interest in nding optimal or near-optimal solutions to these two problems. Many existing heuristic algorithms for MinVC are based on local search strategies. An algorithm called FastVC takes a rst step towards solving the MinVC problem
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19

Chandra, Nagasai. "Node Classification on Relational Graphs using Deep-RGCNs." DigitalCommons@CalPoly, 2021. https://digitalcommons.calpoly.edu/theses/2265.

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Knowledge Graphs are fascinating concepts in machine learning as they can hold usefully structured information in the form of entities and their relations. Despite the valuable applications of such graphs, most knowledge bases remain incomplete. This missing information harms downstream applications such as information retrieval and opens a window for research in statistical relational learning tasks such as node classification and link prediction. This work proposes a deep learning framework based on existing relational convolutional (R-GCN) layers to learn on highly multi-relational data cha
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20

Chamberlain, Benjamin Paul. "Practical challenges of learning and representation for large graphs." Thesis, Imperial College London, 2018. http://hdl.handle.net/10044/1/64783.

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An ever-increasing amount of the humanity's information is being stored in large graphs. The world wide web, digital social networks, e-commerce platforms and chat networks now contain digital traces of the majority of living humans. Many of the most valuable companies ever created are dedicated to organising, managing and extracting useful information from large digital graphs. Machine learning has been shown to be an important tool for automating this task. Discovering scalable machine learning systems, to extract useful information from graphs, is a problem of great practical significance.
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21

Urry, Matthew. "Learning curves for Gaussian process regression on random graphs." Thesis, King's College London (University of London), 2013. https://kclpure.kcl.ac.uk/portal/en/theses/learning-curves-for-gaussian-process-regression-on-random-graphs(c1f5f395-0426-436c-989c-d0ade913423e).html.

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Gaussian processes are a non-parametric method that can be used to learn both regression and classification rules from examples for arbitrary input spaces using the ’kernel trick’. They are well understood for inputs from Euclidean spaces, however, much less research has focused on other spaces. In this thesis I aim to at least partially resolve this. In particular I focus on the case where inputs are defined on the vertices of a graph and the task is to learn a function defined on the vertices from noisy examples, i.e. a regression problem. A challenging problem in the area of non-parametric
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22

Araya, Valdivia Ernesto. "Kernel spectral learning and inference in random geometric graphs." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASM020.

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Cette thèse comporte deux objectifs. Un premier objectif concerne l’étude des propriétés de concentration des matrices à noyau, qui sont fondamentales dans l’ensemble des méthodes à noyau. Le deuxième objectif repose quant à lui sur l’étude des problèmes d’inférence statistique dans le modèle des graphes aléatoires géométriques. Ces deux objectifs sont liés entre eux par le formalisme du graphon, qui permet représenter un graphe par un noyau. Nous rappelons les rudiments du modèle du graphon dans le premier chapitre. Le chapitre 2 présente des bornes précises pour les valeurs propres individue
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23

García, Durán Alberto. "Learning representations in multi-relational graphs : algorithms and applications." Thesis, Compiègne, 2016. http://www.theses.fr/2016COMP2271/document.

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Internet offre une énorme quantité d’informations à portée de main et dans une telle variété de sujets, que tout le monde est en mesure d’accéder à une énorme variété de connaissances. Une telle grande quantité d’information pourrait apporter un saut en avant dans de nombreux domaines (moteurs de recherche, réponses aux questions, tâches NLP liées) si elle est bien utilisée. De cette façon, un enjeu crucial de la communauté d’intelligence artificielle a été de recueillir, d’organiser et de faire un usage intelligent de cette quantité croissante de connaissances disponibles. Heureusement, depui
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24

Lê-Huu, Dien Khuê. "Nonconvex Alternating Direction Optimization for Graphs : Inference and Learning." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLC005/document.

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Cette thèse présente nos contributions àl’inférence et l’apprentissage des modèles graphiquesen vision artificielle. Tout d’abord, nous proposons unenouvelle classe d’algorithmes de décomposition pour résoudrele problème d’appariement de graphes et d’hypergraphes,s’appuyant sur l’algorithme des directionsalternées (ADMM) non convexe. Ces algorithmes sontefficaces en terme de calcul et sont hautement parallélisables.En outre, ils sont également très générauxet peuvent être appliqués à des fonctionnelles d’énergiearbitraires ainsi qu’à des contraintes de correspondancearbitraires. Les expérience
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25

Zappella, G. "LEARNING ON GRAPHS: ALGORITHMS FOR CLASSIFICATION AND SEQUENTIAL DECISIONS." Doctoral thesis, Università degli Studi di Milano, 2014. http://hdl.handle.net/2434/234167.

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In recent years, networked data have become widespread due to the increasing importance of social networks and other web-related applications. This growing interest is driving researchers to design new algorithms for solving important problems that involve networked data. In this thesis we present a few practical yet principled algorithms for learning and sequential decision-making on graphs. Classification of networked data is an important problem that has recently received a great deal of attention from the machine learning community. This is due to its many important practical applicatio
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26

Lee, John Boaz T. "Deep Learning on Graph-structured Data." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-dissertations/570.

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In recent years, deep learning has made a significant impact in various fields – helping to push the state-of-the-art forward in many application domains. Convolutional Neural Networks (CNN) have been applied successfully to tasks such as visual object detection, image super-resolution, and video action recognition while Long Short-term Memory (LSTM) and Transformer networks have been used to solve a variety of challenging tasks in natural language processing. However, these popular deep learning architectures (i.e., CNNs, LSTMs, and Transformers) can only handle data that can be represented a
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27

Pineau, Edouard. "Contributions to representation learning of multivariate time series and graphs." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAT037.

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Les algorithmes de machine learning sont construits pour apprendre, à partir de données, des modèles statistiques de décision ou de prédiction, sur un large panel de tâches. En général, les modèles appris sont des approximations d'un "vrai" modèle de décision, dont la pertinence dépend d'un équilibre entre la richesse du modèle appris, la complexité de la distribution des données et la complexité de la tâche à résoudre à partir des données. Cependant, il est souvent nécessaire d'adopter des hypothèses simplificatrices sur la donnée (e.g. séparabilité linéaire, indépendance des observations, et
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28

Neumann, Marion [Verfasser]. "Learning with Graphs using Kernels from Propagated Information / Marion Neumann." Bonn : Universitäts- und Landesbibliothek Bonn, 2015. http://d-nb.info/1077289626/34.

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29

Moghadasin, Babak. "An Approach on Learning Multivariate Regression Chain Graphs from Data." Thesis, Linköpings universitet, Databas och informationsteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-94019.

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The necessity of modeling is vital for the purpose of reasoning and diagnosing in complex systems, since the human mind might sometimes have a limited capacity and an inability to be objective. The chain graph (CG) class is a powerful and robust tool for modeling real-world applications. It is a type of probabilistic graphical models (PGM) and has multiple interpretations. Each of these interpretations has a distinct Markov property. This thesis deals with the multivariate regression chain graph (MVR-CG) interpretation. The main goal of this thesis is to implement and evaluate the results of t
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30

You, Chang Hun. "Learning patterns in dynamic graphs with application to biological networks." Pullman, Wash. : Washington State University, 2009. http://www.dissertations.wsu.edu/Dissertations/Summer2009/c_you_072309.pdf.

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Thesis (Ph. D.)--Washington State University, August 2009.<br>Title from PDF title page (viewed on Aug. 19, 2009). "School of Electrical Engineering and Computer Science." Includes bibliographical references (p. 114-117).
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31

Kulla-Mader, Julia. "Graphs via Ink: Understanding How the Amount of Non-data Ink in a Graph Affects Perception and Learning." Thesis, School of Information and Library Science, 2007. http://hdl.handle.net/1901/379.

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There is much debate in the design community concerning how to make an easy-to-understand graph. While expert designers recommend including as little non-data ink as possible, there is little empirical evidence to support their arguments. Non-data ink refers to any ink on a graph that is not required to display the graph's data. As a result of the lack of strong evidence concerning how to design graphs, there is widespread confusion when it comes to best practices. This paper describes a preliminary study of graph perception and learning using an eye-tracking system at UNC's School of Informat
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32

Adjei, Seth Akonor. "Refining Learning Maps with Data Fitting Techniques." Digital WPI, 2015. https://digitalcommons.wpi.edu/etd-theses/178.

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Learning maps have been used to represent student knowledge for many years. These maps are usually hand made by experts in a given domain. However, these hand-made maps have not been found to be predictive of student performance. Several methods have been proposed to find bet-ter fitting learning maps. These methods include the Learning Factors Analysis (LFA) model and the Rule-space method. In this thesis we report on the application of one of the proposed operations in the LFA method to a small section of a skill graph and develop a greedy search algorithm for finding better fitting models
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33

Mayo, Quentin R. "Detection of Generalizable Clone Security Coding Bugs Using Graphs and Learning Algorithms." Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1404548/.

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This research methodology isolates coding properties and identifies the probability of security vulnerabilities using machine learning and historical data. Several approaches characterize the effectiveness of detecting security-related bugs that manifest as vulnerabilities, but none utilize vulnerability patch information. The main contribution of this research is a framework to analyze LLVM Intermediate Representation Code and merging core source code representations using source code properties. This research is beneficial because it allows source programs to be transformed into a graphical
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34

Ezzeddine, Diala. "A contribution to topological learning and its application in Social Networks." Thesis, Lyon 2, 2014. http://www.theses.fr/2014LYO22011/document.

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L'Apprentissage Supervisé est un domaine populaire de l'Apprentissage Automatique en progrès constant depuis plusieurs années. De nombreuses techniques ont été développées pour résoudre le problème de classification, mais, dans la plupart des cas, ces méthodes se basent sur la présence et le nombre de points d'une classe donnée dans des zones de l'espace que doit définir le classifieur. Á cause de cela la construction de ce classifieur est dépendante de la densité du nuage de points des données de départ. Dans cette thèse, nous montrons qu'utiliser la topologie des données peut être une bonne
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35

Landrieu, Loïc. "Learning structured models on weighted graphs, with applications to spatial data analysis." Thesis, Paris Sciences et Lettres (ComUE), 2016. http://www.theses.fr/2016PSLEE046/document.

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La modélisation de processus complexes peut impliquer un grand nombre de variables ayant entre elles une structure de corrélation compliquée. Par exemple, les phénomènes spatiaux possèdent souvent une forte régularité spatiale, se traduisant par une corrélation entre variables d’autant plus forte que les régions correspondantes sont proches. Le formalisme des graphes pondérés permet de capturer de manière compacte ces relations entre variables, autorisant la formalisation mathématique de nombreux problèmes d’analyse de données spatiales. La première partie du manuscrit se concentre sur la réso
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36

Preece, Jenny. "Interpreting trends in graphs : a study of 14 and 15 year olds." Thesis, n.p, 1985. http://ethos.bl.uk/.

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37

Yang, Karren Dai. "Learning causal graphs under interventions and applications to single-cell biological data analysis." Thesis, Massachusetts Institute of Technology, 2021. https://hdl.handle.net/1721.1/130806.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Biological Engineering, February, 2021<br>Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2021<br>Cataloged from the official PDF version of thesis.<br>Includes bibliographical references (pages 49-51).<br>This thesis studies the problem of learning causal directed acyclic graphs (DAGs) in the setting where both observational and interventional data is available. This setting is common in biology, where gene regulatory networks can be intervened on using
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38

Chávez, Escalante Diego Alonso 1988. "Semi-supervised learning with graphs methods using signal processing = Métodos de aprendizado semi-supervisionado com grafos usando processamento de sinais." [s.n.], 2014. http://repositorio.unicamp.br/jspui/handle/REPOSIP/275521.

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Orientador: Siome Klein Goldenstein<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação<br>Made available in DSpace on 2018-08-25T19:49:49Z (GMT). No. of bitstreams: 1 ChavezEscalante_DiegoAlonso_M.pdf: 1954210 bytes, checksum: c9a77d2f0545d5517700c34dd6cf3324 (MD5) Previous issue date: 2014<br>Resumo: No aprendizado de máquina, os problemas de classificação de padrões eram tradicionalmente abordados por algoritmos de aprendizado supervisionado que utilizam apenas dados rotulados para treinar-se. Entretanto, os dados rotulados são realmente difíceis de cole
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39

GARBARINO, DAVIDE. "Acknowledging the structured nature of real-world data with graphs embeddings and probabilistic inference methods." Doctoral thesis, Università degli studi di Genova, 2022. http://hdl.handle.net/11567/1092453.

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In the artificial intelligence community there is a growing consensus that real world data is naturally represented as graphs because they can easily incorporate complexity at several levels, e.g. hierarchies or time dependencies. In this context, this thesis studies two main branches for structured data. In the first part we explore how state-of-the-art machine learning methods can be extended to graph modeled data provided that one is able to represent graphs in vector spaces. Such extensions can be applied to analyze several kinds of real-world data and tackle different problems. Here we
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40

Flores, Nicandro. "Counting directed acyclic graphs and its application to Monte Carlo learning of Bayesian networks." Connect to online resource, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1447692.

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41

Navarin, Nicolò <1984&gt. "Learning with Kernels on Graphs: DAG-based kernels, data streams and RNA function prediction." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amsdottorato.unibo.it/6578/1/navarin_nicolo_tesi.pdf.

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In many application domains data can be naturally represented as graphs. When the application of analytical solutions for a given problem is unfeasible, machine learning techniques could be a viable way to solve the problem. Classical machine learning techniques are defined for data represented in a vectorial form. Recently some of them have been extended to deal directly with structured data. Among those techniques, kernel methods have shown promising results both from the computational complexity and the predictive performance point of view. Kernel methods allow to avoid an explicit mapping
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42

Navarin, Nicolò <1984&gt. "Learning with Kernels on Graphs: DAG-based kernels, data streams and RNA function prediction." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amsdottorato.unibo.it/6578/.

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In many application domains data can be naturally represented as graphs. When the application of analytical solutions for a given problem is unfeasible, machine learning techniques could be a viable way to solve the problem. Classical machine learning techniques are defined for data represented in a vectorial form. Recently some of them have been extended to deal directly with structured data. Among those techniques, kernel methods have shown promising results both from the computational complexity and the predictive performance point of view. Kernel methods allow to avoid an explicit mapping
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43

Shah, Shivani. "Graph sparsification and unsupervised machine learning for metagenomic binning." Thesis, Tours, 2019. http://theses.scd.univ-tours.fr/index.php?fichier=2019/shivani.shah_18225.pdf.

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La métagénomique est le domaine de la biologie qui concerne l’étude du contenu génomique des communautés microbiennes directement dans leur environnement. Les données métagénomiques utilisées dans ces travaux de thèse correspondent à des technologies de séquençage produisant des fragments d’ADN courts (reads). L'une des étapes clé de l'analyse des données métagénomiques et développée dans cette étude est le regroupement de reads, appelé également binning. Lors de cette tâche de binning, des groupes (bins) doivent être formés de sorte que chaque groupe soit composé de reads provenant de la même
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44

Bodily, Robert Gordon. "Designing, Developing, and Implementing Real-Time Learning Analytics Student Dashboards." BYU ScholarsArchive, 2018. https://scholarsarchive.byu.edu/etd/7258.

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This document is a multiple-article format dissertation that discusses the iterative design, development, and evaluation processes necessary to create high quality learning analytics dashboard systems. With the growth of online and blended learning environments, the amount of data that researchers and practitioners collect from learning experiences has also grown. The field of learning analytics is concerned with using this data to improve teaching and learning. Many learning analytics systems focus on instructors or administrators, but these tools fail to involve students in the data-driven d
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45

Richard, Émile. "Regularization methods for prediction in dynamic graphs and e-marketing applications." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2012. http://tel.archives-ouvertes.fr/tel-00906066.

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Predicting connections among objects, based either on a noisy observation or on a sequence of observations, is a problem of interest for numerous applications such as recommender systems for e-commerce and social networks, and also in system biology, for inferring interaction patterns among proteins. This work presents formulations of the graph prediction problem, in both dynamic and static scenarios, as regularization problems. In the static scenario we encode the mixture of two different kinds of structural assumptions in a convex penalty involving the L1 and the trace norm. In the dynamic s
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46

Morris, Christopher [Verfasser], Petra [Akademischer Betreuer] Mutzel, and Kristian [Gutachter] Kersting. "Learning with graphs: kernel and neural approaches / Christopher Morris ; Gutachter: Kristian Kersting ; Betreuer: Petra Mutzel." Dortmund : Universitätsbibliothek Dortmund, 2019. http://d-nb.info/1205157441/34.

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47

Limnios, Stratis. "Graph Degeneracy Studies for Advanced Learning Methods on Graphs and Theoretical Results Edge degeneracy: Algorithmic and structural results Degeneracy Hierarchy Generator and Efficient Connectivity Degeneracy Algorithm A Degeneracy Framework for Graph Similarity Hcore-Init: Neural Network Initialization based on Graph Degeneracy." Thesis, Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAX038.

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L'extraction de sous-structures significatives a toujours été un élément clé de l’étude des graphes. Dans le cadre de l'apprentissage automatique, supervisé ou non, ainsi que dans l'analyse théorique des graphes, trouver des décompositions spécifiques et des sous-graphes denses est primordial dans de nombreuses applications comme entre autres la biologie ou les réseaux sociaux.Dans cette thèse, nous cherchons à étudier la dégénérescence de graphe, en partant d'un point de vue théorique, et en nous appuyant sur nos résultats pour trouver les décompositions les plus adaptées aux tâches à accompl
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48

Schwartz, Samuel David. "Machine Learning Techniques as Applied to Discrete and Combinatorial Structures." DigitalCommons@USU, 2019. https://digitalcommons.usu.edu/etd/7542.

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Machine Learning Techniques have been used on a wide array of input types: images, sound waves, text, and so forth. In articulating these input types to the almighty machine, there have been all sorts of amazing problems that have been solved for many practical purposes. Nevertheless, there are some input types which don’t lend themselves nicely to the standard set of machine learning tools we have. Moreover, there are some provably difficult problems which are abysmally hard to solve within a reasonable time frame. This thesis addresses several of these difficult problems. It frames these pro
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49

Weninger, Timothy Edwards. "Link discovery in very large graphs by constructive induction using genetic programming." Thesis, Manhattan, Kan. : Kansas State University, 2008. http://hdl.handle.net/2097/1087.

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Bautista, Ruiz Esteban. "Laplacian Powers for Graph-Based Semi-Supervised Learning." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEN081.

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Les techniques d’apprentissage semi-supervisé basées sur des graphes (G-SSL) permettent d’exploiter des données étiquetées et non étiquetées pour construire de meilleurs classifiers. Malgré de nombreuses réussites, leur performances peuvent encore être améliorées, en particulier dans des situations ou` les graphes ont une faible séparabilité de classes ou quand le nombres de sujets supervisés par l’expert est déséquilibrés. Pour aborder ces limitations on introduit une nouvelle méthode pour G-SSL, appel´ee Lγ -PageRank, qui constitue la principal contribution de cette th`ese. Il s’agit d’une g
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