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Ordinal regression methods: survey and experimental study
(2017-02-03)
Abstract—Ordinal regression problems are those machine learning problems where the objective is to classify patterns using a
categorical scale which shows a natural order between the labels. Many real-world applications ...
Exploitation of Pairwise Class Distances for Ordinal Classification
(2013-07-15)
Ordinal classification refers to classification problems in which the classes have a natural
order imposed on them because of the nature of the concept studied. Some ordinal
classification approaches perform a projection ...
Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery
(2017-03-30)
This paper approaches the problem of weed mapping for precision agriculture,
using imagery provided by Unmanned Aerial Vehicles (UAVs) from sun
ower
and maize crops. Precision agriculture referred to weed control is ...
Borderline kernel based over-sampling
(2017-03-30)
Nowadays, the imbalanced nature of some real-world data
is receiving a lot of attention from the pattern recognition and machine
learning communities in both theoretical and practical aspects, giving
rise to di erent ...
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 ...
Projection based ensemble learning for ordinal regression
(2013-10-08)
The classification of patterns into naturally ordered
labels is referred to as ordinal regression. This paper proposes
an ensemble methodology specifically adapted to this type of
problems, which is based on computing ...
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 ...
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 ...
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 ...