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Mostrando ítems 1-10 de 34
Artículo
On the evolutionary weighting of neighbours and features in the k-nearest neighbour rule
(Elsevier, 2019)
This paper presents an evolutionary method for modifying the behaviour of the k-Nearest-Neighbour clas sifier (kNN) called Simultaneous Weighting of Attributes and Neighbours (SWAN). Unlike other weighting methods, SWAN ...
Artículo
EVOR-STACK: A label-dependent evolutive stacking on remote sensing data fusion
(Elsevier, 2012)
Land use and land covers (LULC) maps are remote sensing products that are used to classify areas into different landscapes. Data fusion for remote sensing is becoming an important tool to improve classical approaches. ...
Artículo
A study of the suitability of autoencoders for preprocessing data in breast cancer experimentation
(Elsevier, 2017)
Breast cancer is the most common cause of cancer death in women. Today, post-transcriptional protein products of the genes involved in breast cancer can be identified by immunohistochemistry. However, this method has ...
Ponencia
Remote mining: from clustering to DTM
(Bournemouth University, 2008)
LIDAR data acquisition is becoming an indispensable task for terrain characterization in large surfaces. In Mediterranean woods this job results hard due to the great variety of heights and forms, as well as sparse ...
Artículo
A Framework for Evaluating Land Use and Land Cover Classification Using Convolutional Neural Networks
(MDPI, 2019)
Analyzing land use and land cover (LULC) using remote sensing (RS) imagery is essential for many environmental and social applications. The increase in availability of RS data has led to the development of new techniques ...
Capítulo de Libro
Improving the k-Nearest Neighbour Rule by an Evolutionary Voting Approach
(Springer, 2014)
This work presents an evolutionary approach to modify the voting system of the k-Nearest Neighbours (kNN). The main novelty of this article lies on the optimization process of voting regardless of the distance of every ...
Artículo
Evolutionary feature selection to estimate forest stand variablesusing LiDAR
(Elsevier, 2014)
Light detection and ranging (LiDAR) has become an important tool in forestry. LiDAR-derived models are mostly developed by means of multiple linear regression (MLR) after stepwise selection of predictors. An increasing ...
Artículo
Autoencoded DNA methylation data to predict breast cancer recurrence: Machine learning models and gene-weight significance
(Elsevier, 2020)
Breast cancer is the most frequent cancer in women and the second most frequent overall after lung cancer. Although the 5-year survival rate of breast cancer is relatively high, recurrence is also common which often ...
Capítulo de Libro
A Comparative Study between Two Regression Methods on LiDAR Data: A Case Study
(Springer, 2011)
Airborne LiDAR (Light Detection and Ranging) has become an excellent tool for accurately assessing vegetation characteristics in forest environments. Previous studies showed empirical relationships between LiDAR and ...
Ponencia
A Preliminary Study of the Suitability of Deep Learning to Improve LiDAR-Derived Biomass Estimation
(Springer, 2016)
Light Detection and Ranging (LiDAR) is a remote sensor able to extract three-dimensional information about forest structure. Bio physical models have taken advantage of the use of LiDAR-derived infor mation to improve ...