2 citations · 3 across the 5 of their papers we have counts for
13 papers
Risk-based regulation for all: The need and a method for a wide adoption solution for data-driven inspection targeting
Celso H. H. Ribas, José C. M. Bermudez
Access to data and data processing, including the use of machine learning techniques, has become significantly easier and cheaper in recent years. Nevertheless, solutions that can…
A Pathology-Based Machine Learning Method to Assist in Epithelial Dysplasia Diagnosis
Karoline da Rocha, José C. M. Bermudez, Elena R. C. Rivero +1
The Epithelial Dysplasia (ED) is a tissue alteration commonly present in lesions preceding oral cancer, being its presence one of the most important factors in the progression towa…
Stochastic Analysis of the Diffusion Least Mean Square and Normalized Least Mean Square Algorithms for Cyclostationary White Gaussian and Non-Gaussian Inputs
Eweda Eweda, Neil J. Bershad, Jose C. M. Bermudez
The diffusion least mean square (DLMS) and the diffusion normalized least mean square (DNLMS) algorithms are analyzed for a network having a fusion center. This structure reduces t…
Graph topology inference with derivative-reproducing property in RKHS: algorithm and convergence analysis
Mircea Moscu, Ricardo A. Borsoi, Cédric Richard +1
In many areas such as computational biology, finance or social sciences, knowledge of an underlying graph explaining the interactions between agents is of paramount importance but…
Tech Report: A Homogeneity-Based Multiscale Hyperspectral Image Representation for Sparse Spectral Unmixing
L. C. Ayres, S. J. M. de Almeida, J. C. M. Bermudez +1
Several approaches have been proposed to solve the spectral unmixing problem in hyperspectral image analysis. Among them the use of sparse regression techniques aims to characteriz…
Coupled Tensor Decomposition for Hyperspectral and Multispectral Image Fusion with Inter-Image Variability
Ricardo Augusto Borsoi, Clémence Prévost, Konstantin Usevich +3
Coupled tensor approximation has recently emerged as a promising approach for the fusion of hyperspectral and multispectral images, reconciling state of the art performance with st…