Jorge Dantas de Melo

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Reinforcement Learning has been an approach successfully applied for solving several problems available in literature. It is usually employed for solving complex problems, such as the ones involving systems with incomplete knowledge, time variant systems, non-linear systems, etc., but it does not mean that it cannot be applied for solving simple problems.(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)
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 reach very good solutions which, however, don’t guarantee the determination of the global optimum. Beyond the inherent difficulties related to the complexity that characterizes(More)
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)
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)