74 citations · 291 across the 20 of their papers we have counts for
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Enhancing Hyper-To-Real Space Projections Through Euclidean Norm Meta-Heuristic Optimization
Luiz C. F. Ribeiro, Mateus Roder, Gustavo H. de Rosa +2
The continuous computational power growth in the last decades has made solving several optimization problems significant to humankind a tractable task; however, tackling some of th…
FEMa-FS: Finite Element Machines for Feature Selection
Lucas Biaggi, João P. Papa, Kelton A. P Costa +2
Identifying anomalies has become one of the primary strategies towards security and protection procedures in computer networks. In this context, machine learning-based methods emer…
PL-kNN: A Parameterless Nearest Neighbors Classifier
Danilo Samuel Jodas, Leandro Aparecido Passos, Ahsan Adeel +1
Demands for minimum parameter setup in machine learning models are desirable to avoid time-consuming optimization processes. The -Nearest Neighbors is one of the most effective…
MaxDropoutV2: An Improved Method to Drop out Neurons in Convolutional Neural Networks
Claudio Filipi Goncalves do Santos, Mateus Roder, Leandro A. Passos +1
In the last decade, exponential data growth supplied the machine learning-based algorithms' capacity and enabled their usage in daily life activities. Additionally, such an improve…
Handling Imbalanced Datasets Through Optimum-Path Forest
Leandro Aparecido Passos, Danilo S. Jodas, Luiz C. F. Ribeiro +3
In the last decade, machine learning-based approaches became capable of performing a wide range of complex tasks sometimes better than humans, demanding a fraction of the time. Suc…
A Metaheuristic-Driven Approach to Fine-Tune Deep Boltzmann Machines
Leandro Aparecido Passos, João Paulo Papa
Deep learning techniques, such as Deep Boltzmann Machines (DBMs), have received considerable attention over the past years due to the outstanding results concerning a variable rang…
PF: Oversampling via Optimum-Path Forest for Breast Cancer Detection
Leandro Aparecido Passos, Danilo Samuel Jodas, Luiz C. F. Ribeiro +2
Breast cancer is among the most deadly diseases, distressing mostly women worldwide. Although traditional methods for detection have presented themselves as valid for the task, the…
Temperature-Based Deep Boltzmann Machines
Leandro Aparecido Passos Junior, Joao Paulo Papa
Deep learning techniques have been paramount in the last years, mainly due to their outstanding results in a number of applications, that range from speech recognition to face-base…