Esta tese propõe uma metodologia baseada em métodos de kernel, teoria fuzzy e probabilidade para tratar conjuntos de dados cujas observações são conjuntos de pontos. As medidas de probabilidade e os conjuntos fuzzy são usados para modelar essas observações. Posteriormente, graças a kernels definidos sobre medidas de probabilidade, ou em conjuntos fuzzy, é feito o mapeamento implícito dessas medidas de probabilidade, ou desses conjuntos fuzzy, para espaços de Hilbert com kernel reproduzível, onde a análise pode ser feita com algum método kernel. Usando essa metodologia, é possível fazer frente a uma ampla gamma de problemas de aprendizado para esses conjuntos de dados. Em particular, a tese apresenta o projeto de modelos de descrição de dados para observações modeladas com medidas de probabilidade. Isso é conseguido graças ao mergulho das medidas de probabilidade nos espaços de Hilbert, e a construção de esferas envolventes mínimas nesses espaços de Hilbert. A tese apresenta como esses modelos podem ser usados como classificadores de uma classe, aplicados na tarefa de detecção de anomalias grupais. No caso que as observações sejam modeladas por conjuntos fuzzy, a tese propõe mapear esses conjuntos fuzzy para os espaços de Hilbert com kernel reproduzível. Isso pode ser feito graças à projeção de novos kernels definidos sobre conjuntos fuzzy. A tese apresenta como esses novos kernels podem ser usados em diversos problemas como classificação, regressão e na definição de distâncias entre conjuntos fuzzy. Em particular, a tese apresenta a aplicação desses kernels em problemas de classificação supervisionada em dados intervalares e teste kernel de duas amostras para dados contendo atributos imprecisos. / This thesis proposes a methodology based on kernel methods, probability measures and fuzzy sets, to analyze datasets whose individual observations are itself sets of points, instead of individual points. Fuzzy sets and probability measures are used to model observations; and kernel methods to analyze the data. Fuzzy sets are used when the observation contain imprecise, vague or linguistic values. Whereas probability measures are used when the observation is given as a set of multidimensional points in a $D$-dimensional Euclidean space. Using this methodology, it is possible to address a wide range of machine learning problems for such datasets. Particularly, this work presents data description models when observations are modeled by probability measures. Those description models are applied to the group anomaly detection task. This work also proposes a new class of kernels, \\emph{the kernels on fuzzy sets}, that are reproducing kernels able to map fuzzy sets to a geometric feature spaces. Those kernels are similarity measures between fuzzy sets. We give from basic definitions to applications of those kernels in machine learning problems as supervised classification and a kernel two-sample test. Potential applications of those kernels include machine learning and patter recognition tasks over fuzzy data; and computational tasks requiring a similarity measure estimation between fuzzy sets.

The automatic extraction of knowledge from the world by a robotic system as human beings interpret their environment through their senses is still an unsolved task in Artificial Intelligence. A robotic agent is in contact with the world through its sensors and other electronic components which obtain and process mainly numerical information. Sonar, infrared and laser sensors obtain distance information. Webcams obtain digital images that are represented internally as matrices of red, blue and green (RGB) colour coordinate values. All this numerical values obtained from the environment need a later interpretation in order to provide the knowledge required by the robotic agent in order to carry out a task. Similarly, light wavelengths with specific amplitude are captured by cone cells of human eyes obtaining also stimulus without meaning. However, the information that human beings can describe and remember from what they see is expressed using words, that is qualitatively. The exact process carried out after our eyes perceive light wavelengths and our brain interpret them is quite unknown. However, a real fact in human cognition is that people go beyond the purely perceptual experience to classify things as members of categories and attach linguistic labels to them. As the information provided by all the electronic components incorporated in a robotic agent is numerical, the approaches that first appeared in the literature giving an interpretation of this information followed a mathematical trend. In this thesis, this problem is addressed from the other side, its main aim is to process these numerical data in order to obtain qualitative information as human beings can do. The research work done in this thesis tries to narrow the gap between the acquisition of low level information by robot sensors and the need of obtaining high level or qualitative information for enhancing human-machine communication and for applying logical reasoning processes based on concepts. Moreover, qualitative concepts can be added a meaning by relating them to others. They can be used for reasoning applying qualitative models that have been developed in the last twenty years for describing and interpreting metrical and mathematical concepts such as orientation, distance, velocity, acceleration, and so on. And they can be also understood by human-users both written and read aloud. The first contributions presented are the definition of a method for obtaining fuzzy distance patterns (which include qualitative distances such as ‘near’, far’, ‘very far’ and so on) from the data obtained by any kind of distance sensors incorporated in a mobile robot and the definition of a factor to measure the dissimilarity between those fuzzy patterns. Both have been applied to the integration of the distances obtained by the sonar and laser distance sensors incorporated in a Pioneer 2 dx mobile robot and, as a result, special obstacles have been detected as ‘glass window’, ‘mirror’, and so on. Moreover, the fuzzy distance patterns provided have been also defuzzified in order to obtain a smooth robot speed and used to classify orientation reference systems into ‘open’ (it defines an open space to be explored) or ‘closed’. The second contribution presented is the definition of a model for qualitative image description (QID) by applying the new defined models for qualitative shape and colour description and the topology model by Egenhofer and Al-Taha [1992] and the orientation models by Hernández [1991] and Freksa [1992]. This model can qualitatively describe any kind of digital image and is independent of the image segmentation method used. The QID model have been tested in two scenarios in robotics: (i) the description of digital images captured by the camera of a Pioneer 2 dx mobile robot and (ii) the description of digital images of tile mosaics taken by an industrial camera located on a platform used by a robot arm to assemble tile mosaics. In order to provide a formal and explicit meaning to the qualitative description of the images generated, a Description Logic (DL) based ontology has been designed and presented as the third contribution. Our approach can automatically process any random image and obtain a set of DL-axioms that describe it visually and spatially. And objects included in the images are classified according to the ontology schema using a DL reasoner. Tests have been carried out using digital images captured by a webcam incorporated in a Pioneer 2 dx mobile robot. The images taken correspond to the corridors of a building at University Jaume I and objects with them have been classified into ‘walls’, ‘floor’, ‘office doors’ and ‘fire extinguishers’ under different illumination conditions and from different observer viewpoints. The final contribution is the definition of a similarity measure between qualitative descriptions of shape, colour, topology and orientation. And the integration of those measures into the definition of a general similarity measure between two qualitative descriptions of images. These similarity measures have been applied to: (i) extract objects with similar shapes from the MPEG7 CE Shape-1 library; (ii) assemble tile mosaics by qualitative shape and colour similarity matching; (iii) compare images of tile compositions; and (iv) compare images of natural landmarks in a mobile robot world for their recognition. The contributions made in this thesis are only a small step forward in the direction of enhancing robot knowledge acquisition from the world. And it is also written with the aim of inspiring others in their research, so that bigger contributions can be achieved in the future which can improve the life quality of our society.

Le milieu hospitalier est un monde à la fois sensible et complexe, sensible parce que la vie humaine est en jeu et complexe parce que les équipements médicaux augmentent en nombre et en complexité technique. Ainsi, afin de préserver le bon état de fonctionnement de ces équipements et à un niveau élevé de disponibilité, leur entretien est devenu l'une des préoccupations majeures des responsables de l’hôpital. L’objectif de cette thèse est de proposer, aux responsables de maintenance biomédicale dans les établissements de soins, des outils d’aide à la décision qui permettent une meilleure maitrise des coûts. Ceci en assurant la sécurité des patients et des utilisateurs et en maintenant des performances optimales de l’ensemble des équipements médicaux. Tout d’abord, une heuristique a été proposée pour le choix de l’internalisation ou de l’externalisation de la maintenance et pour la sélection du contrat adéquat. La sélection du contrat est basée sur un ensemble de critères tout en considérant la contrainte du budget disponible. Ensuite, afin d’améliorer la procédure proposée, nous avons proposé des outils d’aide à la décision multicritère pour le choix adéquat d’une stratégie de maintenance. Pour l’étude de la criticité des équipements médicaux et le choix de la maintenance, sept critères ont été étudiés en proposant un couplage de l’approche AHP « Analytical Hierarchy Process » à la technique TOPSIS « Technique for Order Performance by Similarity to Ideal Solution ». Comme les experts du service de maintenance présentaient une certaine incertitude dans leurs jugements, nous avons intégré l’évaluation linguistique floue dans l’étude de la criticité des équipements et dans la sélection de la stratégie de maintenance (Fuzzy AHP couplée avec Fuzzy TOPSIS). Un modèle mathématique MILP a été développé pour la définition des limites de la criticité afin de caractériser les trois stratégies de maintenance. Le bon choix de ces limites permet d’optimiser le coût de la maintenance en respectant le budget disponible. Enfin, un deuxième modèle mathématique MILP a été développé en se basant sur l’heuristique proposée. Ce modèle permet de sélectionner pour chaque équipement, la stratégie de maintenance, internaliser ou externaliser la maintenance et le type du contrat tout en considérant le budget disponible et la charge/capacité du service maintenance / The hospital is a world that is both sensitive and complex, sensitive because the human life is involved and complex because medical facilities are growing in number and in technical complexity. Then, the problem of the medical equipment maintenance in order to keep them in safe, reliable and with high level of availability has become a major preoccupation of the hospital. The objective of this thesis is to provide tools to help the biomedical maintenance service of the hospital to make decisions that allow a better control of costs, while ensuring patient and user safety and maintaining optimal performance of medical equipment. First, a heuristic has been proposed for the choice of internalization or outsourcing maintenance and for the selection of the appropriate contract. The selection of the contract is based on a set of criteria while considering the available budget constraint. Then, to improve the proposed procedure, we proposed multi-criteria decision-making tools to select the appropriate maintenance strategies. Seven criteria have been designed to study the criticality of medical equipment and the choice of maintenance by providing a coupling of the AHP approach "Analytical Hierarchy Process" with TOPSIS technique "Technique for Order Performance by Similarity to Ideal Solution." As the expert judgments of the maintenance department presented some uncertainty, we integrated the fuzzy language assessment of the criticality of the equipment and the selection of the maintenance strategy (Fuzzy AHP coupled with Fuzzy TOPSIS). A mixed integer linear programming model (MILP) was developed to define thresholds of criticality to characterize the three maintenance strategies. According to these thresholds, maintenance cost can be optimized within the available budget. Finally, a second mixed integer linear programming model (MILP) was developed based on the proposed heuristic. This model allows selecting for each equipment, the maintenance strategy, the internalization or the outsourcing of the maintenance and the type of contract while considering the available budget and the workload / capacity of the maintenance department

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / This work aims to evaluate the performance of the loss of field protection methods in synchronous generators. The methods are introduced to make available in a single text the various ways to protect the synchronous generator against the loss of field. The conventional methods have some problems, and the main of them is the improper operation caused by the power stable oscillations. In order to solve or minimize the relay algorithm malfunction, two new methods of loss of field protection are proposed. The first is designed to increase the operational area of the generator on steady state conditions, bounded by the conventional loss of field protection. It was accomplished by modifying the operational characteristic of the mho relay, which is better coordinated with the generator capability curve. The second makes use of loss of field conception in a fuzzy set theory. With the objective to identify the performance of each one, the methods were evaluated through computational simulations of loss of field and stable power oscillation. The methods were set and evaluated according to the generator parameters (Xd and X'd). Since, tests in three machines with different parameters were carried out. So it was possible to conclude that the methods do not behave the same way for different generators parameters. On the other hand, the proposed technique, which is based on the fuzzy set theory was more efficient and not have been affected by the generator parameters and system considered. / Este trabalho visa avaliar o desempenho dos métodos de proteção contra a perda de excitação em geradores síncronos. Os métodos são apresentados de forma a tornar disponível em um único texto as diversas maneiras de se proteger o gerador síncrono contra a perda do seu sistema de excitação. Os métodos convencionais abordados apresentam alguns problemas, sendo o principal, a operação indevida causada por oscilações estáveis de potência. Visando solucionar ou minimizar estes problemas, dois novos métodos de proteção contra a perda de excitação são propostos. O primeiro, objetivando aumentar a área operacional do gerador em regime permanente, restringida pela proteção contra a perda de excitação convencional, por meio de uma característica operacional modificada, melhor coordenada com a curva de capacidade do gerador. O segundo introduz os fundamentos clássicos da proteção contra a perda de excitação na teoria dos conjuntos nebulosos. Com o objetivo de se identificar o desempenho de cada um, os métodos existentes e os propostos foram avaliados por meio de simulações computacionais de perda de excitação e oscilação estável de potência. Como os métodos possuem ajustes em função dos parâmetros do gerador protegido (Xd e X d), realizaram-se testes com três máquinas de parâmetros distintos. Deste modo, foi possível concluir que os métodos não se comportam da mesma maneira para geradores de diferente porte. Por outro lado, a técnica proposta, que tem como base a teoria dos conjuntos nebulosos se mostrou eficiente e não teve seu desempenho afetado pelos parâmetros do gerador e do sistema considerado.

Made available in DSpace on 2016-06-02T19:05:54Z (GMT). No. of bitstreams: 1 3998.pdf: 3486824 bytes, checksum: f1c040adfdc7d0672bc93a058f8a413d (MD5) Previous issue date: 2011-06-28 / Financiadora de Estudos e Projetos / The goal of this work is to study, expand and evaluate the use of multiobjective genetic algorithms and the iterative rule learning approach in fuzzy system generation, especially, in fuzzy rule-based systems, both in automatic fuzzy rule generation from datasets and in fuzzy sets optimization. This work investigates the use of multi-objective genetic algorithms with a focus on the trade-off between accuracy and interpretability, considered contradictory objectives in the representation of fuzzy systems. With this purpose, we propose and implement an evolutive multi-objective genetic model composed of three stages. In the first stage uniformly distributed fuzzy sets are created. In the second stage, the rule base is generated by using an iterative rule learning approach and a multiobjective genetic algorithm. Finally the fuzzy sets created in the first stage are optimized through a multi-objective genetic algorithm. The proposed model was evaluated with a number of benchmark datasets and the results were compared to three other methods found in the literature. The results obtained with the optimization of the fuzzy sets were compared to the result of another fuzzy set optimizer found in the literature. Statistical comparison methods usually applied in similar context show that the proposed method has an improved classification rate and interpretability in comparison with the other methods. / O objetivo deste trabalho é estudar, expandir e avaliar o uso dos algoritmos genéticos multiobjetivo e a abordagem iterativa na geração de sistemas fuzzy, mais especificamente para sistemas fuzzy baseados em regras, tanto na geração automática da base de regras fuzzy a partir de conjuntos de dados, como a otimização dos conjuntos fuzzy. Esse trabalho investiga o uso dos algoritmos genéticos multiobjetivo com enfoque na questão de balanceamento entre precisão e interpretabilidade, ambos considerados contraditórios entre si na representação de sistemas fuzzy. Com este intuito, é proposto e implementado um modelo evolutivo multiobjetivo genético composto por três etapas. Na primeira etapa são criados os conjuntos fuzzy uniformemente distribuídos. Na segunda etapa é tratada a geração da base de regras usando a abordagem iterativa e um algoritmo genético multiobjetivo. Por fim, na terceira etapa os conjuntos fuzzy criados na primeira etapa são otimizados mediante um algoritmo genético multiobjetivo. O modelo desenvolvido foi avaliado em diversos conjuntos de dados benchmark e os resultados obtidos foram comparados com outros três métodos, que geram regras de classificação, encontrados na literatura. Os resultados obtidos após a otimização dos conjuntos fuzzy foram comparados com resultados de outro otimizador de conjuntos fuzzy encontrado na literatura. Métodos estatísticos de comparação usualmente aplicados em contextos semelhantes mostram uma melhor taxa de classificação e interpretabilidade do método proposto com relação a outros métodos.

Made available in DSpace on 2014-07-29T16:02:20Z (GMT). No. of bitstreams: 1 ALEX NERI GUTIERREZ DISSERTACAO.pdf: 1234288 bytes, checksum: ae65a58b7c2fd793b3c15d44001d82d6 (MD5) Previous issue date: 2012-03-23 / This work has the main objective in the context of the fuzzy theory. Averaging method, differential inclusions are studied; finally this context of the fuzzy theory. / O trabalho tem como objetivo principal, o estudo de um método de averaging em problemas de valor inicial no contexto fuzzy. Com o intuito de facilitar a compreensão do trabalho, faz-se um estudo do, um método de averaging no contexto determinístico, teoria de inclusões diferencias, teoria dos conjuntos fuzzy, inclusões diferenciais fuzzy e finalmente mostra-se o um resultado da validade do método de averaging no contexto fuzzy.

L'objet de cette thèse est de proposer et de développer les différentes mesures multidimensionnelles de la pauvreté. La multidimensionnalité de la pauvreté fait aujourd'hui consensus. Scientifiques, décideurs politiques et professionnels du développement s'accordent pour dire que la seule dimension monétaire (le manque de revenu) ne suffit pas à représenter la pauvreté. En se basant sur les travaux de Sen (Prix Nobel d'Economie) en particulier sur l'approche des capacités, nous proposons quatre mesures différentes de la pauvreté. La première est une mesure monétaire basée sur l'approche utilitaire ; la seconde est une mesure subjective basée sur les expériences des ménages ; la troisième est une mesure multidimensionnelle axiomatique et enfin la dernière est une mesure non axiomatique basée sur la théorie des ensembles flous. Elles sont toutes mises en oeuvre en utilisant les données d'enquêtes EDAM3-IS (Enquête Djiboutienne Auprès des Ménages 2012). Les résultats s'inscrivent dans un contexte de croissance économique que connait Djibouti. Cependant, toutes les mesures utilisées ont montré des grandes disparités régionales entre la capitale et les régions en termes d'infrastructure de base et de bien-être des ménages. Chacune de ses méthodes a fourni des résultats avec différentes interprétations des déterminants de la pauvreté. Cela ne signifie pas qu'il existe une méthode bien meilleure que l'autre, mais chaque approche, dans un contexte particulier, peut-être plus pertinente. Ainsi, l'identification des pauvres en appliquant les différentes mesures de la pauvreté nous a donné un profil différencié. Par conséquent, le décideur doit définir au préalable l'objectif poursuivi dans les politiques de lutte contre la pauvreté. Nous pouvons souligner que l'intégration d'une pondération subjective dans la mesure de la pauvreté est une de nos contributions au développement de mesures multidimensionnelles de la pauvreté. / The aim of this thesis is to propose and develop the various multidimensional measures of poverty. There is a consensus on the multidimensional nature of poverty. Scientists, policy makers and development professionals agree that the monetary dimension (lack of income) is inadequate to represent poverty. On the basis of the work of Sen (Nobel Proze of Economics), particularly on the capability approach, we propose four different measures of poverty. The first one is a monetary measure based on the utilitarian approach ; the second is a subjective measure founded on household experience ; the third is a multidimensional axiomatic measure and the final one is a non-axiomatic measure based on the theory of fuzzy sets. They are implemented using survey data EDAM3-IS (Djiboutian Survey of Households 2012). The esults fall within the framework of economic growth in Djibouti. However, all the measures used have shown great disparities between the capital and the regions in terms of basic infrastructure and household welfare. Each method produced results with different interpretations of the determinants of poverty. This does not mean that there is one method being better than the other but rather each approach, in a particular context, may be more relevant. Thus, identifying the poor by applying the different measures of poverty gave us a clear-cut profile, which implies that the decision-maker must first set the aim in view in the implementation of anti-poverty policies. It can be emphasized that the inclusion of a subjective weighting in the process of measuring of poverty is one of our contributions towards the development of multidimensional measures of poverty.

Plusieurs domaines d'application traitent des objets et des données complexes dont la structure et la sémantique de leurs composants sont des informations importantes pour leur manipulation et leur exploitation. La structure de graphe a été bien souvent adoptée, comme modèles de représentation, dans ces domaines. Elle permet de véhiculer un maximum d'informations, liées à la structure, la sémantique et au comportement de ces objets, nécessaires pour assurer une meilleure représentation et une manipulation efficace. Ainsi, lors d'une comparaison entre deux objets complexes, l'opération d'appariement est appliquée entre les graphes les modélisant. Nous nous sommes intéressés dans cette thèse à l'appariement approximatif qui permet de sélectionner les graphes les plus similaires au graphe d'une requête. L'objectif de notre travail est de contribuer à l'interrogation flexible et personnalisée d'objets complexes modélisés sous forme de graphes pour identifier les graphes les plus pertinents aux besoins de l'utilisateur, exprimés d'une manière partielle ou imprécise. Dans un premier temps, nous avons proposé un cadre de sélection de services Web modélisés sous forme de graphes qui permet (i) d'améliorer le processus d'appariement en intégrant les préférences des utilisateurs et l'aspect structurel des graphes comparés, et (ii) de retourner les services les plus pertinents. Une deuxième méthode d'évaluation de requêtes de recherche de graphes par similarité a également été présentée pour calculer le skyline de graphes d'une requête utilisateur en tenant compte de plusieurs mesures de distance de graphes. Enfin, des approches de raffinement ont été définies pour réduire la taille, souvent importante, du skyline. Elles ont pour but d'identifier et d'ordonner les points skyline qui répondent le mieux à la requête de l'utilisateur. / Several application domains deal with complex objects whose structure and semantics of their components are crucial for their handling. For this, graph structure has been adopted, as a model of representation, in these areas to capture a maximum of information, related to the structure, semantics and behavior of such objects, necessary for effective representation and processing. Thus, when comparing two complex objects, a matching technique is applied between their graph structures. In this thesis, we are interested in approximate matching techniques which constitute suitable tools to automatically find and select the most similar graphs to user graph query. The aim of our work is to develop methods to personalized and flexible querying of repositories of complex objects modeled thanks to graphs and then to return the graphs results that fit best the users ’needs, often expressed partially and in an imprecise way. In a first time, we propose a flexible approach for Web service retrieval that relies both on preference satisfiability and structural similarity between process model graphs. This approach allows (i) to improve the matching process by integrating user preferences and the graph structural aspect, and (ii) to return the most relevant services. A second method for evaluating graph similarity queries is also presented. It retrieves graph similarity skyline of a user query by considering a vector of several graph distance measures instead of a single measure. Thus, graphs which are maximally similar to graph query are returned in an ordered way. Finally, refinement methods have been developed to reduce the size of the skyline when it is of a significant size. They aim to identify and order skyline points that match best the user query.

The result of my dissertation justifies the use of fuzzy-sets theory to make a prediction of cost risk of a PPP project, when there is not enough information available to clearly describe the project, and, when the probability distributions of the variables that characterize the project are unknown. I showed that fuzzy-sets theory and linguistic variables may be effectively used in such a case. In this thesis were classified different types of uncertainty and investigated traditional methods for estimating efficiency of a investment project in conditions of uncertainty. On the basis of the analysis were offered new ways of conducting risk analysis for PPP projects with use of fuzzy sets theory. The main goal was to create an application model for risk assessment of the PPP project which, with a high degree of reliability, suggests a general assessment of situation. The goal set in my work was met. Model of risk assessment of the project proposed by me gives more stable results in comparison with the probabilistic model. For comparison were used different types of probability distribution functions and membership functions. The following conclusions and statements describe the novelty of the work on fuzzy logic and economic theory: develops a method of cash-flow (future expenditure connected with the appearance of risk) modeling of investment project in fuzzy environment, demonstrates the use of fuzzy sets theory in projects analyses and describes how to calculate and interpret this value, demonstrates example of the use of results applied to the analysis of infrastructure development project in Moscow, Russia. The possibility of using this method is not only in the analysis of infrastructure development projects, but also in realization of non-commercial projects by social institutes and government agencies.

Esta tese propõe uma metodologia baseada em métodos de kernel, teoria fuzzy e probabilidade para tratar conjuntos de dados cujas observações são conjuntos de pontos. As medidas de probabilidade e os conjuntos fuzzy são usados para modelar essas observações. Posteriormente, graças a kernels definidos sobre medidas de probabilidade, ou em conjuntos fuzzy, é feito o mapeamento implícito dessas medidas de probabilidade, ou desses conjuntos fuzzy, para espaços de Hilbert com kernel reproduzível, onde a análise pode ser feita com algum método kernel. Usando essa metodologia, é possível fazer frente a uma ampla gamma de problemas de aprendizado para esses conjuntos de dados. Em particular, a tese apresenta o projeto de modelos de descrição de dados para observações modeladas com medidas de probabilidade. Isso é conseguido graças ao mergulho das medidas de probabilidade nos espaços de Hilbert, e a construção de esferas envolventes mínimas nesses espaços de Hilbert. A tese apresenta como esses modelos podem ser usados como classificadores de uma classe, aplicados na tarefa de detecção de anomalias grupais. No caso que as observações sejam modeladas por conjuntos fuzzy, a tese propõe mapear esses conjuntos fuzzy para os espaços de Hilbert com kernel reproduzível. Isso pode ser feito graças à projeção de novos kernels definidos sobre conjuntos fuzzy. A tese apresenta como esses novos kernels podem ser usados em diversos problemas como classificação, regressão e na definição de distâncias entre conjuntos fuzzy. Em particular, a tese apresenta a aplicação desses kernels em problemas de classificação supervisionada em dados intervalares e teste kernel de duas amostras para dados contendo atributos imprecisos. / This thesis proposes a methodology based on kernel methods, probability measures and fuzzy sets, to analyze datasets whose individual observations are itself sets of points, instead of individual points. Fuzzy sets and probability measures are used to model observations; and kernel methods to analyze the data. Fuzzy sets are used when the observation contain imprecise, vague or linguistic values. Whereas probability measures are used when the observation is given as a set of multidimensional points in a $D$-dimensional Euclidean space. Using this methodology, it is possible to address a wide range of machine learning problems for such datasets. Particularly, this work presents data description models when observations are modeled by probability measures. Those description models are applied to the group anomaly detection task. This work also proposes a new class of kernels, \\emph{the kernels on fuzzy sets}, that are reproducing kernels able to map fuzzy sets to a geometric feature spaces. Those kernels are similarity measures between fuzzy sets. We give from basic definitions to applications of those kernels in machine learning problems as supervised classification and a kernel two-sample test. Potential applications of those kernels include machine learning and patter recognition tasks over fuzzy data; and computational tasks requiring a similarity measure estimation between fuzzy sets.