1,720,980 research outputs found
Multi-layer feature fusion and selection from convolutional neural networks for texture classification
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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
L'inférence bayésienne dans le cadre de l'analyse des formes 2D et 3D
No full textThe thesis is divided into two main parts: i) Nonparametric statistics on high-dimensional and functional spaces, and ii) Nonparametric statistics on Riemannian manifolds. In this part, we will summarize the major contributions of the thesis. Nonparametric statistics on high-dimensional and functional spacesIn statistical learning, we introduce a new notion entitled: scalable Gaussian process classifier. The proposal is more general than the usual Gaussian process classifier for representing and classifying data lying on high-dimensional spaces. It is more advantageous for learning the hyper-parameters of the mapping (embedding) that maps initial data into a low-dimensional (feature) space and those of the Gaussian process classifier through its covariance function, jointly, with different optimization methods. The modified covariance function, depending on the embedding and operating on the feature space, is more expressive since the Euclidean metric is more informative in low-dimensional spaces. To summarize, our formulation takes care of non-linearity/high-correlation of data and increases the separability between them thanks to the Representer Theorem. In order to estimate the model's hyper-parameters we usually maximize the marginal likelihood. Unlike regression, the computation of the exact marginal likelihood remains difficult and even impossible in the classification case due to the discrete likelihoods. Thus, we introduce two methods to approximate the non-Gaussian posterior distribution by a Gaussian one in order to improve the efficiency and the scalability of the Gaussian process.For functional and even high-dimensional data, we also introduce the notion of Gaussian processes indexed by probability density functions. We will show how Gaussian processes can be defined into functional spaces, in particular that of density functions endowed with the Fisher-Rao metric. More precisely, we will extend the traditional methods of nonparametric statistics based on Gaussian processes from finite vectors in Euclidean spaces to constrained functions with Riemannian metrics. Our motivation is that several categories of observations can be represented by their density functions with more advantages than initial vector or functional inputs. This choice is very crucial for many reasons. First of all, density functions make the problem formulation more understood when identifying the initial vector inputs or functional data, which are hard to interpret, by their occurrences or their corresponding probabilities. Second, density functions improve the visualization of local data distributions. Finally, when dealing with high dimensional datasets (set of repetitive features), we can visualize them using density functions which would be very helpful to explore the skewness of initial data.Applications: Image classification (breast cancer/metallic boxes/growth charts /maize leaves/animal temperature) and video classification (violence detection).Nonparametric statistics on Riemannian manifoldsIn statistical learning on Riemannian manifold of curves, one of major problems is that of registration. For curve registration, we have to find the optimal deformation in terms of the best reparametrization function (or local distribution) between two curves. The space of reparametrization functions is a group of diffeomorphisms for the composition operation, which makes the optimization task quite complicated due to the structure of the group. In fact, there is no intuitive direction nor an underlying metric (or structure) in this group. (...)La thèse se décompose en deux parties principales: i) Statistiques non paramétriques sur les espaces en grande dimension et fonctionnels, et ii) Statistiques non paramétriques sur les variétés riemanniennes. Dans cette partie, nous allons résumer les contributions majeures de la thèse. Statistiques non paramétriques sur les espaces en grande dimension et fonctionnels Dans le domaine d'apprentissage statistique, nous introduisons une nouvelle notion intitulée: processus gaussien de classification évolutif. Le modèle proposé est plus général que le processus gaussien de classification standard pour représenter et classer des données appartenant à des espaces de grande dimension. Il a l'avantage d'apprendre les hyper-paramètres de la fonction qui transforme les données initiales sur un espace de dimension faible et ceux du processus gaussien de classification à travers sa fonction de covariance à la fois, avec plusieurs méthodes d'optimisation. La fonction de covariance modifiée, définie sur le nouveau espace des données transformées, est plus expressive car la métrique euclidienne devient plus informative. Pour résumer, notre formulation prend en considération la non-linéarité/forte corrélation des données et augmente la séparabilité entre elles grâce au Théorème du représentant. Afin d'estimer les hyper-paramètres du modèle proposé, nous maximisons la vraisemblance marginale. Contrairement à la régression, le calcul de la vraisemblance marginale exacte reste difficile et même impossible dans le cas de classification à cause des vraisemblances discrètes. Ainsi, nous introduisons deux méthodes pour approximer une distribution a posteriori non gaussienne par une gaussienne afin d'améliorer l'efficacité et l'évolutivité du processus gaussien. Pour les données fonctionnelles et même vectorielles en grande dimension, nous introduisons également la notion de processus gaussiens indexé par les fonctions de densité de probabilité. Nous montrerons comment les processus gaussiens peuvent être également définis sur des espaces fonctionnelles, en particulier celle de densités de probabilité muni de la métrique de Fisher-Rao. Plus précisément, nous étendrons les méthodes traditionnelles de statistiques non paramétriques par processus gaussiens de vecteurs finis dans les espaces euclidiens aux espaces des fonctions sous des contraintes munies des métriques riemanniennes. Notre motivation est que plusieurs catégories d'observations peuvent être représentées par des densités de probabilité avec plus d'avantages que des entrées vectorielles ou fonctionnelles brutes. Ce choix est très important pour plusieurs raisons. D'abord, les densités de probabilité permettent de simplifier la formulation du problème en identifiant les données vectorielles ou fonctionnelles initiales, qui sont difficiles à interpréter, par leurs occurrences ou leurs probabilités. Ensuite, les densités de probabilité améliorent la visualisation des distributions locales de données. Enfin, lorsqu'il s'agit des données fortement corrélées (caractéristiques répétitives) nous pouvons plutôt visualiser leurs densités de probabilité pour ajuster l'asymétrie des données initiales.Applications: Classification d'images (cancer du sein/boites métalliques/courbes de croissance/feuilles de maïs/température des animaux) et des vidéos (détection de violence). Statistiques non paramétriques sur les variétés riemanniennes. (...
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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