Kernel classifier construction using orthogonal forward selection and boosting with Fisher ratio class separability measure

A greedy technique is proposed to construct parsimonious kernel classifiers using the orthogonal forward selection method and boosting based on Fisher ratio for class separability measure. Unlike most kernel classification methods, which restrict kernel means to the training input data and use a fixed common variance for all the kernel terms, the proposed technique can tune both the mean vector and diagonal covariance matrix of individual kernel by incrementally maximizing Fisher ratio for class separability measure. An efficient weighted optimization method is developed based on boosting to append kernels one by one in an orthogonal forward selection procedure. Experimental results obtained using this construction technique demonstrate that it offers a viable alternative to the existing state-of-the-art kernel modeling methods for constructing sparse Gaussian radial basis function network classifiers. that generalize well.

Learning spatial filters for multispectral image segmentation

We present a novel filtering method for multispectral satellite image classification. The proposed method learns a set of spatial filters that maximize class separability of binary support vector machine (SVM) through a gradient descent approach. Regularization issues are discussed in detail and a Frobenius-norm regularization is proposed to efficiently exclude uninformative filters coefficients. Experiments carried out on multiclass one-against-all classification and target detection show the capabilities of the learned spatial filters.

Kernel-based mapping of orographic rainfall enhancement in the Swiss Alps as detected by weatherradar

In this paper, we develop a data-driven methodology to characterize the likelihood of orographic precipitation enhancement using sequences of weather radar images and a digital elevation model (DEM). Geographical locations with topographic characteristics favorable to enforce repeatable and persistent orographic precipitation such as stationary cells, upslope rainfall enhancement, and repeated convective initiation are detected by analyzing the spatial distribution of a set of precipitation cells extracted from radar imagery. Topographic features such as terrain convexity and gradients computed from the DEM at multiple spatial scales as well as velocity fields estimated from sequences of weather radar images are used as explanatory factors to describe the occurrence of localized precipitation enhancement. The latter is represented as a binary process by defining a threshold on the number of cell occurrences at particular locations. Both two-class and one-class support vector machine classifiers are tested to separate the presumed orographic cells from the nonorographic ones in the space of contributing topographic and flow features. Site-based validation is carried out to estimate realistic generalization skills of the obtained spatial prediction models. Due to the high class separability, the decision function of the classifiers can be interpreted as a likelihood or susceptibility of orographic precipitation enhancement. The developed approach can serve as a basis for refining radar-based quantitative precipitation estimates and short-term forecasts or for generating stochastic precipitation ensembles conditioned on the local topography.

L’utilisation de la polarimétrie radar et de la décomposition de Touzi pour la caractérisation et la classification des physionomies végétales des milieux humides : le cas du Lac Saint-Pierre.

Les milieux humides remplissent plusieurs fonctions écologiques d’importance et contribuent à la biodiversité de la faune et de la flore. Même s’il existe une reconnaissance croissante sur l’importante de protéger ces milieux, il n’en demeure pas moins que leur intégrité est encore menacée par la pression des activités humaines. L’inventaire et le suivi systématique des milieux humides constituent une nécessité et la télédétection est le seul moyen réaliste d’atteindre ce but. L’objectif de cette thèse consiste à contribuer et à améliorer la caractérisation des milieux humides en utilisant des données satellites acquises par des radars polarimétriques en bande L (ALOS-PALSAR) et C (RADARSAT-2). Cette thèse se fonde sur deux hypothèses (chap. 1). La première hypothèse stipule que les classes de physionomies végétales, basées sur la structure des végétaux, sont plus appropriées que les classes d’espèces végétales car mieux adaptées au contenu informationnel des images radar polarimétriques. La seconde hypothèse stipule que les algorithmes de décompositions polarimétriques permettent une extraction optimale de l’information polarimétrique comparativement à une approche multipolarisée basée sur les canaux de polarisation HH, HV et VV (chap. 3). En particulier, l’apport de la décomposition incohérente de Touzi pour l’inventaire et le suivi de milieux humides est examiné en détail. Cette décomposition permet de caractériser le type de diffusion, la phase, l’orientation, la symétrie, le degré de polarisation et la puissance rétrodiffusée d’une cible à l’aide d’une série de paramètres extraits d’une analyse des vecteurs et des valeurs propres de la matrice de cohérence. La région du lac Saint-Pierre a été sélectionnée comme site d’étude étant donné la grande diversité de ses milieux humides qui y couvrent plus de 20 000 ha. L’un des défis posés par cette thèse consiste au fait qu’il n’existe pas de système standard énumérant l’ensemble possible des classes physionomiques ni d’indications précises quant à leurs caractéristiques et dimensions. Une grande attention a donc été portée à la création de ces classes par recoupement de sources de données diverses et plus de 50 espèces végétales ont été regroupées en 9 classes physionomiques (chap. 7, 8 et 9). Plusieurs analyses sont proposées pour valider les hypothèses de cette thèse (chap. 9). Des analyses de sensibilité par diffusiogramme sont utilisées pour étudier les caractéristiques et la dispersion des physionomies végétales dans différents espaces constitués de paramètres polarimétriques ou canaux de polarisation (chap. 10 et 12). Des séries temporelles d’images RADARSAT-2 sont utilisées pour approfondir la compréhension de l’évolution saisonnière des physionomies végétales (chap. 12). L’algorithme de la divergence transformée est utilisé pour quantifier la séparabilité entre les classes physionomiques et pour identifier le ou les paramètres ayant le plus contribué(s) à leur séparabilité (chap. 11 et 13). Des classifications sont aussi proposées et les résultats comparés à une carte existante des milieux humide du lac Saint-Pierre (14). Finalement, une analyse du potentiel des paramètres polarimétrique en bande C et L est proposé pour le suivi de l’hydrologie des tourbières (chap. 15 et 16). Les analyses de sensibilité montrent que les paramètres de la 1re composante, relatifs à la portion dominante (polarisée) du signal, sont suffisants pour une caractérisation générale des physionomies végétales. Les paramètres des 2e et 3e composantes sont cependant nécessaires pour obtenir de meilleures séparabilités entre les classes (chap. 11 et 13) et une meilleure discrimination entre milieux humides et milieux secs (chap. 14). Cette thèse montre qu’il est préférable de considérer individuellement les paramètres des 1re, 2e et 3e composantes plutôt que leur somme pondérée par leurs valeurs propres respectives (chap. 10 et 12). Cette thèse examine également la complémentarité entre les paramètres de structure et ceux relatifs à la puissance rétrodiffusée, souvent ignorée et normalisée par la plupart des décompositions polarimétriques. La dimension temporelle (saisonnière) est essentielle pour la caractérisation et la classification des physionomies végétales (chap. 12, 13 et 14). Des images acquises au printemps (avril et mai) sont nécessaires pour discriminer les milieux secs des milieux humides alors que des images acquises en été (juillet et août) sont nécessaires pour raffiner la classification des physionomies végétales. Un arbre hiérarchique de classification développé dans cette thèse constitue une synthèse des connaissances acquises (chap. 14). À l’aide d’un nombre relativement réduit de paramètres polarimétriques et de règles de décisions simples, il est possible d’identifier, entre autres, trois classes de bas marais et de discriminer avec succès les hauts marais herbacés des autres classes physionomiques sans avoir recours à des sources de données auxiliaires. Les résultats obtenus sont comparables à ceux provenant d’une classification supervisée utilisant deux images Landsat-5 avec une exactitude globale de 77.3% et 79.0% respectivement. Diverses classifications utilisant la machine à vecteurs de support (SVM) permettent de reproduire les résultats obtenus avec l’arbre hiérarchique de classification. L’exploitation d’une plus forte dimensionalitée par le SVM, avec une précision globale maximale de 79.1%, ne permet cependant pas d’obtenir des résultats significativement meilleurs. Finalement, la phase de la décomposition de Touzi apparaît être le seul paramètre (en bande L) sensible aux variations du niveau d’eau sous la surface des tourbières ouvertes (chap. 16). Ce paramètre offre donc un grand potentiel pour le suivi de l’hydrologie des tourbières comparativement à la différence de phase entre les canaux HH et VV. Cette thèse démontre que les paramètres de la décomposition de Touzi permettent une meilleure caractérisation, de meilleures séparabilités et de meilleures classifications des physionomies végétales des milieux humides que les canaux de polarisation HH, HV et VV. Le regroupement des espèces végétales en classes physionomiques est un concept valable. Mais certaines espèces végétales partageant une physionomie similaire, mais occupant un milieu différent (haut vs bas marais), ont cependant présenté des différences significatives quant aux propriétés de leur rétrodiffusion.

Fully complex-valued radial basis function networks: orthogonal least squares regression and classification

We consider a fully complex-valued radial basis function (RBF) network for regression and classification applications. For regression problems, the locally regularised orthogonal least squares (LROLS) algorithm aided with the D-optimality experimental design, originally derived for constructing parsimonious real-valued RBF models, is extended to the fully complex-valued RBF (CVRBF) network. Like its real-valued counterpart, the proposed algorithm aims to achieve maximised model robustness and sparsity by combining two effective and complementary approaches. The LROLS algorithm alone is capable of producing a very parsimonious model with excellent generalisation performance while the D-optimality design criterion further enhances the model efficiency and robustness. By specifying an appropriate weighting for the D-optimality cost in the combined model selecting criterion, the entire model construction procedure becomes automatic. An example of identifying a complex-valued nonlinear channel is used to illustrate the regression application of the proposed fully CVRBF network. The proposed fully CVRBF network is also applied to four-class classification problems that are typically encountered in communication systems. A complex-valued orthogonal forward selection algorithm based on the multi-class Fisher ratio of class separability measure is derived for constructing sparse CVRBF classifiers that generalise well. The effectiveness of the proposed algorithm is demonstrated using the example of nonlinear beamforming for multiple-antenna aided communication systems that employ complex-valued quadrature phase shift keying modulation scheme. (C) 2007 Elsevier B.V. All rights reserved.

Variance Reduction in Analytical Chemistry : New Numerical Methods in Chemometrics and Molecular Simulation

This thesis is based on five papers addressing variance reduction in different ways. The papers have in common that they all present new numerical methods. Paper I investigates quantitative structure-retention relationships from an image processing perspective, using an artificial neural network to preprocess three-dimensional structural descriptions of the studied steroid molecules. Paper II presents a new method for computing free energies. Free energy is the quantity that determines chemical equilibria and partition coefficients. The proposed method may be used for estimating, e.g., chromatographic retention without performing experiments. Two papers (III and IV) deal with correcting deviations from bilinearity by so-called peak alignment. Bilinearity is a theoretical assumption about the distribution of instrumental data that is often violated by measured data. Deviations from bilinearity lead to increased variance, both in the data and in inferences from the data, unless invariance to the deviations is built into the model, e.g., by the use of the method proposed in paper III and extended in paper IV. Paper V addresses a generic problem in classification; namely, how to measure the goodness of different data representations, so that the best classifier may be constructed. Variance reduction is one of the pillars on which analytical chemistry rests. This thesis considers two aspects on variance reduction: before and after experiments are performed. Before experimenting, theoretical predictions of experimental outcomes may be used to direct which experiments to perform, and how to perform them (papers I and II). After experiments are performed, the variance of inferences from the measured data are affected by the method of data analysis (papers III-V).

Manifold learning and the quantum Jensen-Shannon divergence kernel

The quantum Jensen-Shannon divergence kernel [1] was recently introduced in the context of unattributed graphs where it was shown to outperform several commonly used alternatives. In this paper, we study the separability properties of this kernel and we propose a way to compute a low-dimensional kernel embedding where the separation of the different classes is enhanced. The idea stems from the observation that the multidimensional scaling embeddings on this kernel show a strong horseshoe shape distribution, a pattern which is known to arise when long range distances are not estimated accurately. Here we propose to use Isomap to embed the graphs using only local distance information onto a new vectorial space with a higher class separability. The experimental evaluation shows the effectiveness of the proposed approach. © 2013 Springer-Verlag.

Quasi-Potency and Cyclic subgroup separability

We study the profinite topology on discrete groups and in particular the property of cyclic subgroup separability. We investigate the class of quasi-potent, cyclic subgroup separable groups, producing many examples and showing how it behaves with respect to certain group constructions.

Building binary-tree-based multiclass classifiers using separability measures

Various popular machine learning techniques, like support vector machines, are originally conceived for the solution of two-class (binary) classification problems. However, a large number of real problems present more than two classes. A common approach to generalize binary learning techniques to solve problems with more than two classes, also known as multiclass classification problems, consists of hierarchically decomposing the multiclass problem into multiple binary sub-problems, whose outputs are combined to define the predicted class. This strategy results in a tree of binary classifiers, where each internal node corresponds to a binary classifier distinguishing two groups of classes and the leaf nodes correspond to the problem classes. This paper investigates how measures of the separability between classes can be employed in the construction of binary-tree-based multiclass classifiers, adapting the decompositions performed to each particular multiclass problem. (C) 2010 Elsevier B.V. All rights reserved.

Early treatment of Class III malocclusion: 10-year clinical follow-up

Angle Class III malocclusion has been a challenge for researchers concerning diagnosis, prognosis and treatment. It has a prevalence of 5% in the Brazilian population, and may have a genetic or environmental etiology. This malocclusion can be classified as dentoalveolar, skeletal or functional, which will determine the prognosis. Considering these topics, the aim of this study was to describe and discuss a clinical case with functional Class III malocclusion treated by a two-stage approach (interceptive and corrective), with a long-term follow-up. In this case, the patient was treated with a chincup and an Eschler arch, used simultaneously during 14 months, followed by corrective orthodontics. It should be noticed that, in this case, initial diagnosis at the centric relation allowed visualizing the anterior teeth in an edge-to-edge relationship, thereby favoring the prognosis. After completion of the treatment, the patient was followed for a 10-year period, and stability was observed. The clinical treatment results showed that it is possible to achieve favorable outcomes with early management in functional Class III malocclusion patients.

Class II malocclusion occlusal severity description

OBJECTIVES: It is well known that the efficacy and the efficiency of a Class II malocclusion treatment are aspects closely related to the severity of the dental anteroposterior discrepancy. Even though, sample selection based on cephalometric variables without considering the severity of the occlusal anteroposterior discrepancy is still common in current papers. In some of them, when occlusal parameters are chosen, the severity is often neglected. The purpose of this study is to verify the importance given to the classification of Class II malocclusion, based on the criteria used for sample selection in a great number of papers published in the orthodontic journal with the highest impact factor. MATERIAL AND METHODS: A search was performed in PubMed database for full-text research papers referencing Class II malocclusion in the history of the American Journal of Orthodontics and Dentofacial Orthopedics (AJO-DO). RESULTS: A total of 359 papers were retrieved, among which only 72 (20.06%) papers described the occlusal severity of the Class II malocclusion sample. In the other 287 (79.94%) papers that did not specify the anteroposterior discrepancy severity, description was considered to be crucial in 159 (55.40%) of them. CONCLUSIONS: Omission in describing the occlusal severity demands a cautious interpretation of 44.29% of the papers retrieved in this study.

Segmental LeFort I osteotomy for treatment of a class III malocclusion with temporomandibular disorder

This article reports the case of a 19-year-old young man with Class III malocclusion and posterior crossbite with concerns about temporomandibular disorder (TMD), esthetics and functional problems. Surgical-orthodontic treatment was carried out by decompensation of the mandibular incisors and segmentation of the maxilla in 4 pieces, which allowed expansion and advancement. Remission of the signs and symptoms occurred after surgical-orthodontic intervention. The maxillary dental arch presented normal transverse dimension. Satisfactory static and functional occlusion and esthetic results were achieved and remained stable. Three years after the surgical-orthodontic treatment, no TMD sign or symptom was observed and the occlusal results had not changed. When vertical or horizontal movements of the maxilla in the presence of moderate maxillary constriction are necessary, segmental LeFort I osteotomy can be an important part of treatment planning.

Orthodontic-surgical treatment of class III malocclusion with mandibular asymmetry

Class III skeletal malocclusion may present several etiologies, among which maxillary deficiency is the most frequent. Bone discrepancy may have an unfavorable impact on esthetics, which is frequently aggravated by the presence of accentuated facial asymmetries. This type of malocclusion is usually treated with association of Orthodontics and orthognathic surgery for correction of occlusion and facial esthetics. This report presents the treatment of a patient aged 15 years and 1 month with Class III skeletal malocclusion, having narrow maxilla, posterior open bite on the left side, anterior crossbite and unilateral posterior crossbite, accentuated negative dentoalveolar discrepancy in the maxillary arch, and maxillary and mandibular midline shift. Clinical examination also revealed maxillary hypoplasia, increased lower one third of the face, concave bone and facial profiles and facial asymmetry with mandibular deviation to the left side. The treatment was performed in three phases: presurgical orthodontic preparation, orthognathic surgery and orthodontic finishing. In reviewing the patient's final records, the major goals set at the beginning of treatment were successfully achieved, providing the patient with adequate masticatory function and pleasant facial esthetics.

Bottle feeding, increased overjet and Class 2 primary canine relationship: is there any association?

The aim of this study was to investigate the association between bottle feeding and prevalence rates of increased overjet and Class 2 primary canine relationship. The sample consisted of 911 children (461 boys, 450 girls) aged 3 (13.9%), 4 (40.8%), 5 (34%) and 6 (11.3%) years, with complete primary dentition. Information about nutritive and nonnutritive (pacifier and/or digit) sucking habits was collected through questionnaires. Three calibrated dentists (κ: 0.9-1.0 and Rs > 0.90) performed the clinical assessments. The children were divided into four groups: G1 - not bottle-fed; G2 - exclusively bottle-fed; G3 - breast- and bottle-fed, bottle feeding ceased before 3 years of age; and G4 - breast- and bottle-fed, bottle feeding ceased between 3 and 4 years of age. Associations between nutritive and nonnutritive sucking behaviors and the malocclusions studied were analyzed by multiple binary logistic regression (α= 0.05). The frequencies of increased overjet were: 25.3% (G1), 38.8% (G2), 39.2% (G3) and 47.8% (G4). The percentages of Class 2 canine relationship were: 27.9% (G1), 48.8% (G2), 43.4% (G3) and 43% (G4). No significant effect of bottle feeding was found. The chances of diagnosing increased overjet (O.R. = 4.42, p < 0.001) and Class 2 canine relationship (O.R. = 4.02, p < 0.001) were greater for children with pacifier and/or digit-sucking habits, compared to those without a history of nonnutritive sucking behavior. It may be suggested that bottle feeding alone is not directly associated with higher prevalence rates of increased overjet and Class 2 canine relationship in the primary dentition.

Emergence of Klebsiella pneumoniae carrying the novel extended-spectrum 'beta'-lactamase gene variants 'bla IND. SHV-40', 'bla IND. TEM-116' and the class I integron-associated 'bla IND. GES-7' in Brazil

A clinical Klebsiella pneumoniae isolate carrying the extended-spectrum beta-lactamase gene variants bla(SHV-40), bla(TEM-116) and bla(GES-7) was recovered. Cefoxitin and ceftazidime activity was most affected by the presence of these genes and an additional resistance to trimethoprim-sulphamethoxazole was observed. The bla(GES-7) gene was found to be inserted into a class 1 integron. These results show the emergence of novel bla(TEM) and bla(SHV) genes in Brazil. Moreover, the presence of class 1 integrons suggests a great potential for dissemination of bla(GES) genes into diverse nosocomial pathogens. Indeed, the bla(GES-7) gene was originally discovered in Enterobacter cloacae in Greece and, to our knowledge, has not been reported elsewhere