DECSI - Departamento de Computação e Sistemas de Informação

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Browsing DECSI - Departamento de Computação e Sistemas de Informação by Subject "Classification"

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    Combined weightless neural network FPGA architecture for deforestation surveillance and visual navigation of UAVs.
    (2020) Torres, Vitor Angelo Maria Ferreira; Jaimes, Brayan Rene Acevedo; Ribeiro, Eduardo S.; Braga, Mateus T.; Shiguemori, Elcio Hideiti; Velho, Haroldo Fraga de Campos; Torres, Luiz Carlos Bambirra; Braga, Antônio de Pádua
    This work presents a combined weightless neural network architecture for deforestation surveillance and visual navigation of Unmanned Aerial Vehicles (UAVs). Binary images, which are required for position estimation and UAV navigation, are provided by the deforestation surveillance circuit. Learned models are evaluated in a real UAV flight over a green countryside area, while deforestation surveillance is assessed with an Amazon forest benchmarking image data. Small utilization percentage of Field Programmable Gate Arrays (FPGAs) allows for a higher degree of parallelization and block processing of larger regions of input images.
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    Combined weightless neural network FPGA architecture for deforestation surveillance and visual navigation of UAVs.
    (2020) Torres, Vitor Angelo Maria Ferreira; Jaimes, Brayan Rene Acevedo; Ribeiro, Eduardo da Silva; Braga, Mateus Taulois; Shiguemori, Elcio Hideit; Velho, Haroldo Fraga de Campos; Torres, Luiz Carlos Bambirra; Braga, Antônio Pádua
    This work presents a combined weightless neural network architecture for deforestation surveillance and visual navigation of Unmanned Aerial Vehicles (UAVs). Binary images, which are required for position estimation and UAV navigation, are provided by the deforestation surveillance circuit. Learned models are evaluated in a real UAV flight over a green countryside area, while deforestation surveillance is assessed with an Amazon forest benchmarking image data. Small utilization percentage of Field Programmable Gate Arrays (FPGAs) allows for a higher degree of parallelization and block processing of larger regions of input images.
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    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 Pádua
    This 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.
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    Multi-objective decision in machine learning.
    (2016) Medeiros, Talles Henrique de; Rocha, Honovan Paz; Torres, Frank Sill; Takahashi, Ricardo Hiroshi Caldeira; Braga, Antônio de Pádua
    Thiswork presents a novel approach for decisionmaking for multi-objective binary classification problems. The purpose of the decision process is to select within a set of Pareto-optimal solutions, one model that minimizes the structural risk (generalization error). This new approach utilizes a kind of prior knowledge that, if available, allows the selection of a model that better represents the problem in question. Prior knowledge about the imprecisions of the collected data enables the identification of the region of equivalent solutions within the set of Pareto-optimal solutions. Results for binary classification problems with sets of synthetic and real data indicate equal or better performance in terms of decision efficiency compared to similar approaches.
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    Multi-objective neural network model selection with a graph-based large margin approach.
    (2022) Torres, Luiz Carlos Bambirra; Castro, Cristiano Leite de; Rocha, Honovan Paz; Almeida, Gustavo Matheus de; Braga, Antônio de Pádua
    This work presents a new decision-making strategy for multi-objective learning problem of artificial neural networks (ANN). The proposed decision-maker searches for the solution that minimizes a margin-based validation error amongst Pareto set solutions. The proposal is based on a geometric approximation to find the large margin (distance) of separation among the classes. Several benchmarks commonly available in the literature were used for testing. The obtained results showed that the proposal is more efficient in controlling the generalization capacity of neural models than other learning machines. It yields smooth (noise robustness) and well-fitted models straightforwardly, i.e., without the necessity of parameter set definition in advance or validation data use, as often required by learning machines.