Detecting synchronization in spatially extended discrete systems by complexity measurements.
Dichotomie aléatoire et séparation linéaire
Discrimination binaire non paramétrique. Méthodes d'estimation du paramètre de lissage
Discrimination par l'ajustement de variables indicatrices
Dissimilarités de type sphérique et positionnement multidimensionnel normé
Dissimilarites de type spherique et positionnement multidimensionnel normé
Our concern here, is the characterization of dissimilarity indexes defined over finite sets, whose spatial representation is spherical. Consequently, we propose a methodology (Normed MultiDimensional Scaling) to determine the spherical euclidean representation of a set of items best accounting for the initial dissimilarity between items. This methodology has the advantage of being graphically readable on individual qualities of projection like the normed PCA, of which it constitutes a generalization....
Dissimilarités multivoies et généralisations d'hypergraphes sans triangles
Les dissimilarités multivoies sont une généralisation naturelle des dissimilarités usuelles deux voies. Dans ce papier, des classes de dissimilarités multivoies sont étudiées, ainsi que des modèles de passage d'un nombre de voies donné à un autre nombre de voies. Une application à la spécification de systèmes classifiants a conduit à une bijection entre une classe de dissimilarités multivoies et une famille de systèmes stratifiés de classifccation.
Distance à centre additive
Effect of choice of dissimilarity measure on classification efficiency with nearest neighbor method
In this paper we will precisely analyze the nearest neighbor method for different dissimilarity measures, classical and weighed, for which methods of distinguishing were worked out. We will propose looking for weights in the space of discriminant coordinates. Experimental results based on a number of real data sets are presented and analyzed to illustrate the benefits of the proposed methods. As classical dissimilarity measures we will use the Euclidean metric, Manhattan and post office metric....
Effect of prior probabilities on the classificatory performance of parametric and mathematical programming approaches to the two-group discriminant problem.
Eigenanalysis and metric multidimensional scaling on hierarchical structures.
The known hierarchical clustering scheme is equivalent to the concept of ultrametric distance. Every distance can be represented in a spatial model using multidimensional scaling. We relate both classes of representations of proximity data in an algebraic way, obtaining some results and relations on clusters and the eigenvalues of the inner product matrix for an ultrametric distance. Principal coordinate analysis on an ultrametric distance gives two classes of independent coordinates, describing...
Elliptische Streuflächen ebener Punkthaufen.
Empirical significance test of the goodness-of-fit for some pyramidal clustering procedures.
Through a series of simulation tests by Monte Carlo methods, some aspects relating to the inference concerning pyramidal trees built by the maximum and minimum methods are considered. In this sense, the quantiles of the γ-Goodman-Kruskal statistic allow us to tabulate a significance test of the goodness-of-fit of a pyramidal clustering procedure. On the other side, the pyramidal method of maximum is observed to be clearly better (more efficient) than that of the minimum in terms of the expected...
Employing different loss functions for the classification of images via supervised learning
Supervised learning methods are powerful techniques to learn a function from a given set of labeled data, the so-called training data. In this paper the support vector machines approach is applied to an image classification task. Starting with the corresponding Tikhonov regularization problem, reformulated as a convex optimization problem, we introduce a conjugate dual problem to it and prove that, whenever strong duality holds, the function to be learned can be expressed via the dual optimal solutions....
Estimating from cross-sectional categorical data subject to misclassification and double sampling: moment-based, maximum likelihood and quasi-likelihood approaches.
Étude des matrices de proximité rectangulaires en vue de la classification
Étude des puissances d'une distance
Étude exhaustive de l'algorithme de réallocation-recentrage dans un cas simple
Études sur le polyèdre des partitions
Evolutionary hierarchical clustering technique.