Jorge Dantas de Melo

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Support vector machines are one of the most employed methods of pattern classification, and the Adaboost algorithm is an effective way of improving the performance of the weak learners that compose the ensemble. In this article, we propose to create an Adaboost-based ensemble of SVM, by altering the Gaussian width parameter of the RBF-SVM. Using data sets(More)
In the process of searching for better solutions, a metaheuristic can be guided to regions of promising solutions using the acquisition of information on the problem under study. In this work this is done through the use of reinforcement learning. The performance of a metaheuristic can also be improved using multiple search trajectories, which act(More)
There are a lot of different methods in pattern classification, in which one of the most popular is the Support Vector Machine. Lots of tools have been developed to improve SVM classification, mainly the development of new classifying methods and the employment of SVM ensembles. Meanwhile, evolutionary algorithms are recognized tools to solve optimization(More)
Techniques of optimization, known as metaheuristics, have achieved success in the resolution of many problems classified as NP-hard. These methods use non-deterministic approaches that find good solutions which, however, do not guarantee the determination of the global optimum. Beyond the inherent difficulties related to the complexity that characterizes(More)
This paper presents a FPGA based approach for a modular architecture of Fuzzy Neural Networks (FNN) to embed with easily different topologies set up. The project is based on a Takagi - Hayashi (T-H) method for the construction and tuning of fuzzy rules, this is commonly referred as neural network driven fuzzy reasoning. The proposed architecture approach(More)
This paper proposes an algorithm for clustering using an information-theoretic based criterion. The cross entropy between elements in different clusters is used as a measure of quality of the partition. The proposed algorithm uses " classical " clustering algorithms to initialize some small regions (auxiliary clusters) that will be merged to construct the(More)
This paper presents a modeling of a system designed to monitor a moving object from images captured by a camera. The research was focused on defining the steps necessary to the functioning of systems, they are: capture and image processing, pattern recognition with artificial neural networks and seek the best path for moving the camera, using reinforcement(More)
This paper shows a comparison between two clustering algorithms that use divergence measures to aid the clustering task. Both algorithms take a N-dimensional data set and uses competitive neural networks to separate them into isotropic clusters. Those clusters are then grouped based on a divergence measure. In this paper we compare that procedure using two(More)