Navegando por Autor "Coelho, Frederico Gualberto Ferreira"
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Item Enhancing performance of Gabriel graph-based classifiers by a hardware co-processor for embedded system applications.(2020) Arias Garcia, Janier; Mafra, Augusto Amaral; Gade, Liliane dos Reis; Coelho, Frederico Gualberto Ferreira; Castro, Cristiano Leite de; Torres, Luiz Carlos Bambirra; Braga, Antônio de PáduaIt is well known that there is an increasing interest in edge computing to reduce the distance between cloud and end devices, especially for Machine Learning (ML) methods. However, when related to latency-sensitive applications, little work can be found in ML literature on suitable embedded systems implementations. This paper presents new ways to implement the decision rule of a large margin classifier based on Gabriel graphs as well as an efficient implementation of this on an embedded system. The proposed approach uses the nearest neighbor method as the decision rule, and the implementation starts from an RTL pipeline architecture developed for binary large margin classifiers and proposes the integration in a hardware/software co-design. Results showed that the proposed approach was statistically similar to the classifier and had a speedup factor of up to 8x compared to the classifier executed in software, with performance suitable for ML latency-sensitive applications.Item Large margin gaussian mixture classifier with a Gabriel graph geometric representation of data set structure.(2020) Torres, Luiz Carlos Bambirra; Castro, Cristiano Leite de; Coelho, Frederico Gualberto Ferreira; Braga, Antônio de PáduaThis brief presents a geometrical approach for obtaining large margin classifiers. The method aims at exploring the geometrical properties of the data set from the structure of a Gabriel graph, which represents pattern relations according to a given distance metric, such as the Euclidean distance. Once the graph is generated, geometrical support vectors (SVs) (analogous to support vector machines (SVMs) SVs) are obtained in order to yield the final large margin solution from a Gaussian mixture model. Experiments with 20 data sets have shown that the solutions obtained with the proposed method are statistically equivalent to those obtained with SVMs. However, the present method does not require optimization and can also be extended to large data sets using the cascade SVM concept.