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Dataset on object identification training in a construction site [Dataset]
(Universidad de LimaPE, 2021)
Conjunto de datos utilizados en el entrenamiento de un algoritmo para el reconocimiento de maquinaria encontrada en proyectos de construcción. Consta de 1046 imágenes capturadas mediante cámaras fijas en diferentes puntos ...
Improving the accuracy of the optimum-path forest supervised classifier for large datasets
(2010-12-15)
In this work, a new approach for supervised pattern recognition is presented which improves the learning algorithm of the Optimum-Path Forest classifier (OPF), centered on detection and elimination of outliers in the ...
Improving the accuracy of the optimum-path forest supervised classifier for large datasets
(2010-12-15)
In this work, a new approach for supervised pattern recognition is presented which improves the learning algorithm of the Optimum-Path Forest classifier (OPF), centered on detection and elimination of outliers in the ...
Is it possible to make pixel-based radar image classification user-friendly?
(2011-11-16)
In this paper we would like to shed light the problem of efficiency and effectiveness of image classification in large datasets. As the amount of data to be processed and further classified has increased in the last years, ...
Is it possible to make pixel-based radar image classification user-friendly?
(2011-11-16)
In this paper we would like to shed light the problem of efficiency and effectiveness of image classification in large datasets. As the amount of data to be processed and further classified has increased in the last years, ...
A new parallel training algorithm for optimum-path forest-based learning
(Springer Verlag, 2017)
In this work, we present a new parallel-driven approach to speed up Optimum-Path Forest (OPF) training phase. In addition, we show how to make OPF up to five times faster for training using a simple parallel-friendly data ...
Decision Tree based Classifiers for Large Datasets
(Computación y Sistemas; Vol. 17 No. 1, 2013-03-06)
Abstract: In this paper, several algorithms have been developed for building decision trees from large datasets. These algorithms overcome some restrictions of the most recent algorithms in the state of the art. Three of ...
Transfer learning between texture classification tasks using convolutional neural networks
(IEEE, 2015)
Convolutional Neural Networks (CNNs) have set the state-of-the-art in many computer vision tasks in recent years. For this type of model, it is common to have millions of parameters to train, commonly requiring large ...