1,720,963 research outputs found
Modèles prédictifs par agrégation consensuelle et applications
No full textThree important projects are studied in this thesis. The first project is "KFC : a clusterwise supervised learning procedure based on aggregation of distances". It is a three-step procedure for constructing prediction in supervised statistical learning problems. KFC stands for K-means/Fit/Combining. Several performances of the method are illustrated in this part on several synthetic and real energy data. The second project is "A kernel-based consensual aggregation method for regression", which is inspired by the numerical experiments of the previous project. The method is a generalization of consensual aggregation method introduced by Biau et al. (2016) to regular kernel-based setting. The consistency inheritance property of the method is derived, and is confirmed through many numerical experiments on simulated and real datasets. Lastly, the third project is a study of consensual aggregation method on randomly projected high-dimensional features of predictions. The aggregation scheme is composed of two steps: the high-dimensional features of predictions are randomly projected into a small subspace in the first step, then the aggregation method is applied on the projected features in the second step. We numerically show that the consensual aggregation method upholds its performance on very large and highly correlated features of predictions. Moreover, we theoretically show that the performance of the method is almost preserved in much smaller subspaces of projection, with high probability. This shows the robustness of the method in a sense that several types of predictive models can be plainly constructed and directly combined without model selection or cross-validation technique.Trois projets importants sont étudiés dans cette thèse. Le premier projet est "KFC : Une procédure d'apprentissage supervisé par cluster basée sur l'agrégation des distances". C'est une méthodologie en trois étapes pour construire des prédictions dans les problèmes d'apprentissage supervisé. KFC signifie K-means/Fit/Combining. Plusieurs performances numériques de la méthode sont illustrées sur des jeux de données synthétiques et énergétiques réelles. Le deuxième projet est une méthode d'agrégation consensuelle à noyau pour la régression, qui s'inspire des expériences numériques du projet précédent. La méthode est une agrégation consensuelle généralisée de Biau et al. (2016) sur une fonction noyau régulière. Elle agrège un nombre d'estimateurs de régression. La propriété d'héritage de consistance de la méthode est prouvée, et confirmée par plusieurs expériences numériques sur des jeux de données simulés et réels. Le dernier projet est l'étude de l'agrégation consensuelle basée sur le noyau en grandes dimensions des vecteurs des prédictions. Le schéma d'agrégation est composé de deux étapes : les prédictions de grande dimension sont projetées de manière aléatoire dans un sous-espace plus petit dans la première étape, et la méthode d'agrégation est appliquée aux vecteurs des prédictions projetées dans la deuxième étape. Nous montrons numériquement que l'agrégation consensuelle étudiée dans le projet précédent maintient ses performances avec un grand nombre d'estimateurs, fortement corrélées. De plus, nous montrons théoriquement que les performances du schéma d'agrégation complet dans des sous-espaces de projection beaucoup plus petits, avec une grande probabilité. Elle montre la robustesse de la méthode dans le sens où plusieurs types de modèles prédictifs peuvent être simplement construits et directement combinés sans aucune technique de sélection de modèle ou de validation croisée
Predictive models by consensual aggregation and applications
No full textTrois projets importants sont étudiés dans cette thèse. Le premier projet est "KFC : Une procédure d'apprentissage supervisé par cluster basée sur l'agrégation des distances". C'est une méthodologie en trois étapes pour construire des prédictions dans les problèmes d'apprentissage supervisé. KFC signifie K-means/Fit/Combining. Plusieurs performances numériques de la méthode sont illustrées sur des jeux de données synthétiques et énergétiques réelles. Le deuxième projet est une méthode d'agrégation consensuelle à noyau pour la régression, qui s'inspire des expériences numériques du projet précédent. La méthode est une agrégation consensuelle généralisée de Biau et al. (2016) sur une fonction noyau régulière. Elle agrège un nombre d'estimateurs de régression. La propriété d'héritage de consistance de la méthode est prouvée, et confirmée par plusieurs expériences numériques sur des jeux de données simulés et réels. Le dernier projet est l'étude de l'agrégation consensuelle basée sur le noyau en grandes dimensions des vecteurs des prédictions. Le schéma d'agrégation est composé de deux étapes : les prédictions de grande dimension sont projetées de manière aléatoire dans un sous-espace plus petit dans la première étape, et la méthode d'agrégation est appliquée aux vecteurs des prédictions projetées dans la deuxième étape. Nous montrons numériquement que l'agrégation consensuelle étudiée dans le projet précédent maintient ses performances avec un grand nombre d'estimateurs, fortement corrélées. De plus, nous montrons théoriquement que les performances du schéma d'agrégation complet dans des sous-espaces de projection beaucoup plus petits, avec une grande probabilité. Elle montre la robustesse de la méthode dans le sens où plusieurs types de modèles prédictifs peuvent être simplement construits et directement combinés sans aucune technique de sélection de modèle ou de validation croisée.Three important projects are studied in this thesis. The first project is "KFC : a clusterwise supervised learning procedure based on aggregation of distances". It is a three-step procedure for constructing prediction in supervised statistical learning problems. KFC stands for K-means/Fit/Combining. Several performances of the method are illustrated in this part on several synthetic and real energy data. The second project is "A kernel-based consensual aggregation method for regression", which is inspired by the numerical experiments of the previous project. The method is a generalization of consensual aggregation method introduced by Biau et al. (2016) to regular kernel-based setting. The consistency inheritance property of the method is derived, and is confirmed through many numerical experiments on simulated and real datasets. Lastly, the third project is a study of consensual aggregation method on randomly projected high-dimensional features of predictions. The aggregation scheme is composed of two steps: the high-dimensional features of predictions are randomly projected into a small subspace in the first step, then the aggregation method is applied on the projected features in the second step. We numerically show that the consensual aggregation method upholds its performance on very large and highly correlated features of predictions. Moreover, we theoretically show that the performance of the method is almost preserved in much smaller subspaces of projection, with high probability. This shows the robustness of the method in a sense that several types of predictive models can be plainly constructed and directly combined without model selection or cross-validation technique
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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