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

Ambiente de textualização no ensino a distância: o caso do fórum educacional

This research deals with textualization issues present in educational forums in distance learning environment. The research aims to analyze textualization regarding communication practices between tutors and distance learning students. Specifically the research aims to verify if the educational forum is considered pertinent for knowledge construction as well as identify subject´s behavior in e-Proinfo environment. The research also aims to understand the dynamics of the teaching and learning techniques related to the forum´s printed material. This is done in order to acknowledge discourse on behalf of subjects through the presented educational assignments. In order to address the issue, the work dealt with the relations present in distance learning forums, the forms in which the assignments are made, the way social actors interact and how this debate happens in the virtual environment. The research emphasized an educational forum used in a higher education institution at Rio Grande do Norte/Brazil. Thus the research corpus is composed by messages that were posted in the forum in the module called computer material . This module is one of the last in a set of six modules that are part of The Basic Cycle for Media Training promoted by the Center for Distance Learning in a public university at Rio Grande do Norte/Brazil. The research deals with a qualitative type approach in the perspectives of Merriam (1988), Cresswell (1994) and Minayo (1996). In order to achieve this analysis, the research dealt with theoretical landmarks related to distance learning present in (Silva, 2008; Brait, 1993; Sperbe and Wilson, 1986; Marquesi and Elias 2008 as well as Xavier, 2005, amongst others. As for aspects related to media and technological perspectives present in the forum, the research dealt with (Baranov, 1989; Neuner, 1981; Kearsley and Moore, 1996). Textualization was dealt according to (Marcuschi, 2008; Costa Val, 2004) and the conceptions and functions regarding tutors was seen according to (Salgado, 2002). In the conclusion and recommendations it was seen that these discussions present relevant contributions to distance learning and go beyond the practical universe present in electronical interaction. In the final considerations it is pointed out that this research is relevant for areas such as applied linguistics and presents guidelines for those involved in continuous education and aim meaningful knowledge that is coherent with distance learning education

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