Implementação de classificador de tarefas mentais baseado em EEG

BARBOSA, André F. ; SOUZA, Bryan C. ; PEREIRA JUNIOR, Antônio ; MEDEIROS, Adelardo A. D.de, . Implementação de Classificador de Tarefas Mentais Baseado em EEG. In: CONGRESSO BRASILEIRO DE REDES NEURAIS, 9., 2009, Ouro Preto, MG. Anais... Ouro Preto, MG, 2009

An investigation of Hebbian phase sequences as assembly graphs

Hebb proposed that synapses between neurons that fire synchronously are strengthened, forming cell assemblies and phase sequences. The former, on a shorter scale, are ensembles of synchronized cells that function transiently as a closed processing system; the latter, on a larger scale, correspond to the sequential activation of cell assemblies able to represent percepts and behaviors. Nowadays, the recording of large neuronal populations allows for the detection of multiple cell assemblies. Within Hebb's theory, the next logical step is the analysis of phase sequences. Here we detected phase sequences as consecutive assembly activation patterns, and then analyzed their graph attributes in relation to behavior. We investigated action potentials recorded from the adult rat hippocampus and neocortex before, during and after novel object exploration (experimental periods). Within assembly graphs, each assembly corresponded to a node, and each edge corresponded to the temporal sequence of consecutive node activations. The sum of all assembly activations was proportional to firing rates, but the activity of individual assemblies was not. Assembly repertoire was stable across experimental periods, suggesting that novel experience does not create new assemblies in the adult rat. Assembly graph attributes, on the other hand, varied significantly across behavioral states and experimental periods, and were separable enough to correctly classify experimental periods (Naïve Bayes classifier; maximum AUROCs ranging from 0.55 to 0.99) and behavioral states (waking, slow wave sleep, and rapid eye movement sleep; maximum AUROCs ranging from 0.64 to 0.98). Our findings agree with Hebb's view that assemblies correspond to primitive building blocks of representation, nearly unchanged in the adult, while phase sequences are labile across behavioral states and change after novel experience. The results are compatible with a role for phase sequences in behavior and cognition.

Projeto em Áreas Consolidadas de Patrimônio Cultural: Propostas para a Construção de uma Metodologia de Ensino

Le thème du patrimoine culturel architectural et urbain continue d avoir une place importante dans le milieu technique et scientifique. Le concept s est élargi et aujourd hui comprend différentes procédures de projets d intervention. L importance accordée au thème amène à l inclusion de la matière de techniques rétrospectives et aux contenus qui en sont liés: conservation, restauration, restructuration et reconstruction d édifices et ensembles urbains, dans les parcours des cours d architecture et d urbanisme au Brésil établies par le Ministère de l Education Nationale (MEC) dans les années quatre-vingt-dix, postérieurement incorporés dans les directrices disciplinaires nationales. Nous partons des discussions théoriques et conceptuelles du Domaine du Patrimoine Culturel, ainsi que des principales théories pédagogiques d enseignements et d apprentissage articulées au projet. Dans ce contexte les objectifs principaux de cette thèse consistent à systématiser et à analyser les principales procédures méthodologiques contribuant pour la construction de méthodes d enseignement tournée vers des activités pratiques dans ce domaine. Pour cela, la recherche a été systématisée dans une approche à deux niveaux. En ce qui concerne le premier, basé sur des données secondaires, neuf cours d architecture et urbanisme ont étés identifiés entre institutions publiques d enseignement supérieur dont huit brésiliennes et une française, considérées représentatives en ce qui concerne les pratiques d enseignement de projet et de patrimoine culturel. Trente disciplines dédiées à la matière ont été également reconnues initialement, et postérieurement, cinq disciplines qui possèdent un emploi du temps dédié à la pratique de projet ont aussi été reconnues. Dans le deuxième cas, basée sur des données primaires, ont étés analysées les méthodologies et les stratégies d enseignement de projet basées sur les définitions des matières et des autres éléments des plans de travail avec des observations, des entrevues et des questionnaires en trois ateliers. Par rapport aux résultats nous avons constaté que toutes les écoles possèdent les contenus de la matière, mais peu d entre elles privilégient la relation du projet appliqué au patrimoine culturel. Nous avons constaté que les questions des projets dans ce contexte, même s elles sont considérées complexes, ont privilégié le listage et l analyse du site. L atelier qui intègre les fondements des théories de préservation, l histoire de l architecture et urbanisme et techniques anciennes et actuelles, est mis en valeur comme un modèle cohérent avec les propositions d intégration des connaissances théoriques et pratiques du projet appliqué à la discipline. Basé sur ces constatations il est possible de démontrer quatre étapes du projet appliqué au patrimoine culturel: 1ª) les fondements généraux qui concernent les bases théoriques sur la préservation, histoire et technique rétrospective, par exemple, l appropriation de lois et normes et la sensibilisation de l élève sur les questions de patrimoine culturel; 2ª) le contacte avec la réalité qui inclut l appropriation du problème à partir de ces acteurs, de ces échelles, de cette lecture de site et l analyse de l objet d étude; 3ª) le développement de la proposition qui inclut programmes (fonctions existantes et propositions), définitions du partit (types d intervention), conception (hypothèse et discussion) et définition de proposition; 4ª) la finalisation du projet qui consiste à développer la proposition avec sa représentation graphique et sa présentation finale. Nous concluons que le projet en Domaine du Patrimoine Culturel demande une attention spéciale et doit être présent dans les cursus considérant les principes généraux nécessaires à la formation de l élève. Le binôme projet / patrimoine signifie avoir dans le cursus universitaire les contenus et questions nécessaires les connaissances, les variables et possibilités existantes dans le projet appliqué au patrimoine culturel de façon à ce que ces connaissances soient incorporées dans l exercice de projet et n apparaissent pas comme un simple contenu théorique sans articulation avec la pratique. Naturellement ces conclusions n épuisent pas la réflexion sur la question. Nous espérons que les analyses faites contribuent à définir des méthodologies d enseignements capables d êtres vérifiées et testées dans la pratique en salle de cours, et puisse collaborer avec les nouvelles recherches surtout celles qui ont pour but des nouvelles théories pédagogiques d enseignement apprentissage du projet en Domaine du Patrimoine Culturel

Interesses da habitação social : políticas e processos no Rio Grande do Norte

En 2003, le gouvernement brésilien (gestion Lula) a initié une nouvelle phase dans son histoire de l habitation, en intensifiant les constructions de logements sociaux au Brésil. Un tel accroissement a eut des répercussions tant en ville comme à la campagne, et fût marqué dans le Rio Grande do Norte, par la production a grande échelle d ensembles d habitations, dans les programmes de Gouvernement. Afin de viabiliser ces transformations, des instruments politiques, financiers et de gestion ont étés articulés conjointement, utilisant la répétition d une typologie d édification, comme modèle, accompagnée de la reproduction d une morphologie dans les constructions de logements sociaux. Afin de comprendre ce processus nous introduisons une recherche urbanistique et socio-économique du problème du logement social au Brésil, en cherchant à mettre en relation les aspects techniques avec les questions historique, professionnelles et culturelles, éléments complémentaires. Notre analyse cherche a identifier comment les politiques de gestion et financement officielles (administrées dans sa grande majorité par la Caisse Économique Fédérale -CEF-), influencent le processus de conception de projets, en provoquant les répétitions de type/morphologiques, déjà citées. Basée sur l observation directe au cour de deux expériences différenciées pour du logement social en milieu rural, au Rio Grande do Norte, nous montrerons aussi certaines limitations et possibilités des acteurs sociaux, face aux agents et politiques officielles pour le logement social au Brésil, proposant des solutions alternatives standardisées qui caractérisent le résultat des projets financées et gérés par la CEF. Nos principales références théoriques et méthodologiques sont Nabil Bonduki (1998), David Harvey (2009,1982), Henry Lefèbvre (1970), Ermínia Maricato (2010, 2009, 2000, 1987) et Raquel Rolnik (2010, 2009, 2008, 1997)

Classificação de padrões através de um comitê de máquinas aprimorado por aprendizagem por reforço

Reinforcement learning is a machine learning technique that, although finding a large number of applications, maybe is yet to reach its full potential. One of the inadequately tested possibilities is the use of reinforcement learning in combination with other methods for the solution of pattern classification problems. It is well documented in the literature the problems that support vector machine ensembles face in terms of generalization capacity. Algorithms such as Adaboost do not deal appropriately with the imbalances that arise in those situations. Several alternatives have been proposed, with varying degrees of success. This dissertation presents a new approach to building committees of support vector machines. The presented algorithm combines Adaboost algorithm with a layer of reinforcement learning to adjust committee parameters in order to avoid that imbalances on the committee components affect the generalization performance of the final hypothesis. Comparisons were made with ensembles using and not using the reinforcement learning layer, testing benchmark data sets widely known in area of pattern classification

Algoritmos genéticos aplicados a um comitê de LS-SVM em problemas de classificação

The pattern classification is one of the machine learning subareas that has the most outstanding. Among the various approaches to solve pattern classification problems, the Support Vector Machines (SVM) receive great emphasis, due to its ease of use and good generalization performance. The Least Squares formulation of SVM (LS-SVM) finds the solution by solving a set of linear equations instead of quadratic programming implemented in SVM. The LS-SVMs provide some free parameters that have to be correctly chosen to achieve satisfactory results in a given task. Despite the LS-SVMs having high performance, lots of tools have been developed to improve them, mainly the development of new classifying methods and the employment of ensembles, in other words, a combination of several classifiers. In this work, our proposal is to use an ensemble and a Genetic Algorithm (GA), search algorithm based on the evolution of species, to enhance the LSSVM classification. In the construction of this ensemble, we use a random selection of attributes of the original problem, which it splits the original problem into smaller ones where each classifier will act. So, we apply a genetic algorithm to find effective values of the LS-SVM parameters and also to find a weight vector, measuring the importance of each machine in the final classification. Finally, the final classification is obtained by a linear combination of the decision values of the LS-SVMs with the weight vector. We used several classification problems, taken as benchmarks to evaluate the performance of the algorithm and compared the results with other classifiers

Conceitos e técnicas da mecânica estatística e termodinâmica aplicados ao estudo dos grafos aleatórios

This dissertation briefly presents the random graphs and the main quantities calculated from them. At the same time, basic thermodynamics quantities such as energy and temperature are associated with some of their characteristics. Approaches commonly used in Statistical Mechanics are employed and rules that describe a time evolution for the graphs are proposed in order to study their ergodicity and a possible thermal equilibrium between them

Otimização em comitês de classificadores: uma abordagem baseada em filtro para seleção de subconjuntos de atributos

Traditional applications of feature selection in areas such as data mining, machine learning and pattern recognition aim to improve the accuracy and to reduce the computational cost of the model. It is done through the removal of redundant, irrelevant or noisy data, finding a representative subset of data that reduces its dimensionality without loss of performance. With the development of research in ensemble of classifiers and the verification that this type of model has better performance than the individual models, if the base classifiers are diverse, comes a new field of application to the research of feature selection. In this new field, it is desired to find diverse subsets of features for the construction of base classifiers for the ensemble systems. This work proposes an approach that maximizes the diversity of the ensembles by selecting subsets of features using a model independent of the learning algorithm and with low computational cost. This is done using bio-inspired metaheuristics with evaluation filter-based criteria

Utilizando Pesos estáticos e dinâmicos em sistemas multi-classificadores com diferentes níveis de diversidade

Although some individual techniques of supervised Machine Learning (ML), also known as classifiers, or algorithms of classification, to supply solutions that, most of the time, are considered efficient, have experimental results gotten with the use of large sets of pattern and/or that they have a expressive amount of irrelevant data or incomplete characteristic, that show a decrease in the efficiency of the precision of these techniques. In other words, such techniques can t do an recognition of patterns of an efficient form in complex problems. With the intention to get better performance and efficiency of these ML techniques, were thought about the idea to using some types of LM algorithms work jointly, thus origin to the term Multi-Classifier System (MCS). The MCS s presents, as component, different of LM algorithms, called of base classifiers, and realized a combination of results gotten for these algorithms to reach the final result. So that the MCS has a better performance that the base classifiers, the results gotten for each base classifier must present an certain diversity, in other words, a difference between the results gotten for each classifier that compose the system. It can be said that it does not make signification to have MCS s whose base classifiers have identical answers to the sames patterns. Although the MCS s present better results that the individually systems, has always the search to improve the results gotten for this type of system. Aim at this improvement and a better consistency in the results, as well as a larger diversity of the classifiers of a MCS, comes being recently searched methodologies that present as characteristic the use of weights, or confidence values. These weights can describe the importance that certain classifier supplied when associating with each pattern to a determined class. These weights still are used, in associate with the exits of the classifiers, during the process of recognition (use) of the MCS s. Exist different ways of calculating these weights and can be divided in two categories: the static weights and the dynamic weights. The first category of weights is characterizes for not having the modification of its values during the classification process, different it occurs with the second category, where the values suffers modifications during the classification process. In this work an analysis will be made to verify if the use of the weights, statics as much as dynamics, they can increase the perfomance of the MCS s in comparison with the individually systems. Moreover, will be made an analysis in the diversity gotten for the MCS s, for this mode verify if it has some relation between the use of the weights in the MCS s with different levels of diversity

Uma Análise Comparativa entre Sistemas de Combinação de Classificadores com Distribuição Vertical dos Dados

In systems that combine the outputs of classification methods (combination systems), such as ensembles and multi-agent systems, one of the main constraints is that the base components (classifiers or agents) should be diverse among themselves. In other words, there is clearly no accuracy gain in a system that is composed of a set of identical base components. One way of increasing diversity is through the use of feature selection or data distribution methods in combination systems. In this work, an investigation of the impact of using data distribution methods among the components of combination systems will be performed. In this investigation, different methods of data distribution will be used and an analysis of the combination systems, using several different configurations, will be performed. As a result of this analysis, it is aimed to detect which combination systems are more suitable to use feature distribution among the components

Uma análise da aplicação do modelo de Rede Neural RePART em Comitês de classificadores

RePART (Reward/Punishment ART) is a neural model that constitutes a variation of the Fuzzy Artmap model. This network was proposed in order to minimize the inherent problems in the Artmap-based model, such as the proliferation of categories and misclassification. RePART makes use of additional mechanisms, such as an instance counting parameter, a reward/punishment process and a variable vigilance parameter. The instance counting parameter, for instance, aims to minimize the misclassification problem, which is a consequence of the sensitivity to the noises, frequently presents in Artmap-based models. On the other hand, the use of the variable vigilance parameter tries to smoouth out the category proliferation problem, which is inherent of Artmap-based models, decreasing the complexity of the net. RePART was originally proposed in order to minimize the aforementioned problems and it was shown to have better performance (higer accuracy and lower complexity) than Artmap-based models. This work proposes an investigation of the performance of the RePART model in classifier ensembles. Different sizes, learning strategies and structures will be used in this investigation. As a result of this investigation, it is aimed to define the main advantages and drawbacks of this model, when used as a component in classifier ensembles. This can provide a broader foundation for the use of RePART in other pattern recognition applications

Dilema da diversidade-acurácia: um estudo empírico no contexto de multiclassificadores

Multi-classifier systems, also known as ensembles, have been widely used to solve several problems, because they, often, present better performance than the individual classifiers that form these systems. But, in order to do so, it s necessary that the base classifiers to be as accurate as diverse among themselves this is also known as diversity/accuracy dilemma. Given its importance, some works have investigate the ensembles behavior in context of this dilemma. However, the majority of them address homogenous ensemble, i.e., ensembles composed only of the same type of classifiers. Thus, motivated by this limitation, this thesis, using genetic algorithms, performs a detailed study on the dilemma diversity/accuracy for heterogeneous ensembles

Uma Análise de métodos de distriubuição de atributos em comitês de classificadores

The objective of the researches in artificial intelligence is to qualify the computer to execute functions that are performed by humans using knowledge and reasoning. This work was developed in the area of machine learning, that it s the study branch of artificial intelligence, being related to the project and development of algorithms and techniques capable to allow the computational learning. The objective of this work is analyzing a feature selection method for ensemble systems. The proposed method is inserted into the filter approach of feature selection method, it s using the variance and Spearman correlation to rank the feature and using the reward and punishment strategies to measure the feature importance for the identification of the classes. For each ensemble, several different configuration were used, which varied from hybrid (homogeneous) to non-hybrid (heterogeneous) structures of ensemble. They were submitted to five combining methods (voting, sum, sum weight, multiLayer Perceptron and naïve Bayes) which were applied in six distinct database (real and artificial). The classifiers applied during the experiments were k- nearest neighbor, multiLayer Perceptron, naïve Bayes and decision tree. Finally, the performance of ensemble was analyzed comparatively, using none feature selection method, using a filter approach (original) feature selection method and the proposed method. To do this comparison, a statistical test was applied, which demonstrate that there was a significant improvement in the precision of the ensembles

Seleção de atributos em comitês de classificadores utilizando algoritmos genéticos

Classifier ensembles are systems composed of a set of individual classifiers and a combination module, which is responsible for providing the final output of the system. In the design of these systems, diversity is considered as one of the main aspects to be taken into account since there is no gain in combining identical classification methods. The ideal situation is a set of individual classifiers with uncorrelated errors. In other words, the individual classifiers should be diverse among themselves. One way of increasing diversity is to provide different datasets (patterns and/or attributes) for the individual classifiers. The diversity is increased because the individual classifiers will perform the same task (classification of the same input patterns) but they will be built using different subsets of patterns and/or attributes. The majority of the papers using feature selection for ensembles address the homogenous structures of ensemble, i.e., ensembles composed only of the same type of classifiers. In this investigation, two approaches of genetic algorithms (single and multi-objective) will be used to guide the distribution of the features among the classifiers in the context of homogenous and heterogeneous ensembles. The experiments will be divided into two phases that use a filter approach of feature selection guided by genetic algorithm

Comitês de grupamento aplicados a dados de expressão gênica

The main goal of this work is to investigate the suitability of applying cluster ensemble techniques (ensembles or committees) to gene expression data. More specifically, we will develop experiments with three diferent cluster ensembles methods, which have been used in many works in literature: coassociation matrix, relabeling and voting, and ensembles based on graph partitioning. The inputs for these methods will be the partitions generated by three clustering algorithms, representing diferent paradigms: kmeans, ExpectationMaximization (EM), and hierarchical method with average linkage. These algorithms have been widely applied to gene expression data. In general, the results obtained with our experiments indicate that the cluster ensemble methods present a better performance when compared to the individual techniques. This happens mainly for the heterogeneous ensembles, that is, ensembles built with base partitions generated with diferent clustering algorithms