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Algoritmos de aprendizaje evolutivo y estadístico para la determinación de mapas de malas hierbas utilizando técnicas de teledetección
(2013-10-08)
Este trabajo aborda la resolución de problemas de
clasificación binaria utilizando una metodología
híbrida que combina la regresión logística y
modelos evolutivos de redes neuronales de
unidades producto. Para estimar ...
Regresión no lineal mediante la evolución de modelos Híbridos de Redes Neuronales
(Comité Organizador de MAEB'05, 2005)
El presente trabajo es una primera aproximación a
la formación de modelos de redes neuronales con
unidades ocultas de tipo híbrido (sigmoides,
producto) que siendo aproximadores universales,
puedan utilizarse como ...
A weed monitoring system using UAV-imagery and the Hough transform
(2015)
Usually, crops require the use of herbicides as a useful manner of controlling the
quality and quantity of crop production. Although there are weed-free areas, the most
common approach is to broadcast herbicides entirely ...
Detection of early warning signals in paleoclimate data using a genetic time series segmentation algorithm
(2017)
This paper proposes a time series segmentation algorithm combining a clustering technique and a genetic algorithm to automatically find segments sharing common statistical characteristics in paleoclimate time series. The ...
Semi-supervised Learning for Ordinal Kernel Discriminant Analysis
(Elsevier, 2016)
Ordinal classication considers those classication problems where the labels of
the variable to predict follow a given order. Naturally, labelled data is scarce
or di_cult to obtain in this type of problems because, in ...
Object-Based Image Classification of Summer Crops with Machine Learning Methods
(MDPI, 2014)
The strategic management of agricultural lands involves crop field monitoring each year. Crop discrimination via remote sensing is a complex task, especially if different crops have a similar spectral response and cropping ...
Building Suitable Datasets for Soft Computing and Machine Learning Techniques from Meteorological Data Integration: A Case Study for Predicting Significant Wave Height and Energy Flux
(MDPI, 2021)
Meteorological data are extensively used to perform environmental learning. Soft Computing (SC) and Machine Learning (ML) techniques represent a valuable support in many research areas, but require datasets containing ...
An ordinal CNN approach for the assessment of neurological damage in Parkinson’s disease patients
(Elsevier, 2021)
3D image scans are an assessment tool for neurological damage in Parkinson’s disease (PD) patients. This diagnosis process can be automatized to help medical staff through Decision Support Systems (DSSs), and Convolutional ...
Error-Correcting Output Codes in the Framework of Deep Ordinal Classification
(Springer, 2022)
Automatic classification tasks on structured data have been revolutionized by Convolutional Neural Networks (CNNs), but the focus has been on binary and nominal classification tasks. Only recently, ordinal classification ...
A Review of Classification Problems and Algorithms in Renewable Energy Applications
(MDPI, 2016)
Classification problems and their corresponding solving approaches constitute one of the
fields of machine learning. The application of classification schemes in Renewable Energy (RE) has
gained significant attention in ...