Graph-based approach for airborne light detection and ranging segmentation

By David López Vilariño, José Carlos Cabaleiro, Jorge Martínez, Francisco Fernandez Rivera, Tomás Fernandez Pena. 2014

370 citations recorded by the source corpus.

Subfields: Environmental Engineering, Geology, Signal Processing

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Citations in the corpus

Analyzing Federated Learning through an Adversarial Lens (2018, methodology): “The second dataset is the UCI Adult Census dataset 3 for which we use a fully connected neural network achieving 84.8% accuracy on the test set (Fernández-Delgado et al., 2014) for the model architecture.”
Measuring the Evolution of a Scientific Field through Citation Frames (2018, methodology): “Forest classifier, which is robust to overfitting even with large numbers of features (Fernández-Delgado et al., 2014).”
Precinct or Prejudice? Understanding Racial Disparities in New York City's Stop-and-Frisk Policy (2016, methodology): “Finally, we repeat our primary analysis with a random forest classifier, considered to be one of the best statistical methods for large-scale classification [Fernández-Delgado et al. (2014)].”
Deep learning with feature embedding for compound-protein interaction prediction (2016, methodology): “In all computational experiments, random forest [55], one of the most powerful classifiers [56], was also used as a baseline method to demonstrate the need to use the deep neural network model.”
It’s going to be okay: Measuring Access to Support in Online Communities (2018, positive): “A Random Forest classifier was trained on all 23,903 features; random forests are robust to overfitting even with large numbers of features, making them suitable for this high-dimensional feature space (Fernández-Delgado et al., 2014).”
Data Analysis Strategies in Medical Imaging (2018, methodology): “Myriad supervised and unsupervised methods exist in the machine learning literature derived using different statistical assumptions(42,45,46).”
Neural Random Forests (2016, background): “They work fast, generally exhibit a substantial improvement over single tree learners, and yield generalization error rates that often rank among the best (see, e.g., Fern´andez-Delgado et al., 2014).”
Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks (2019, methodology): “Note this number is much less than the number of hyperparameter of Gaussian kernel reported in Fern´andez-Delgado et al. (2014) where they tune hyperparameters with 500 combinations.”
RAPID: Rapid and Precise Interpretable Decision Sets (2019, background): “For instance, it has been noticed that the best models (with respect to classification accuracy), over hundreds of datasets are not easily interpreted [3].”
A comparative analysis of gradient boosting algorithms (2019, methodology): “Actually, one of the main advantages of random forest is that it is almost parameter-free or at least, the default parameter setting has a remarkable performance on average [8].”
Fair Forests: Regularized Tree Induction to Minimize Model Bias (2017, background): “In a study of over one-hundred datasets, Random Forests were found to be one of the best performing approaches — even when no hyperparameter tuning is done [12].”
End-to-End Learning of Decision Trees and Forests (2019, background): “They have shown their effectiveness on a variety of classiﬁcation tasks (Barros et al. 2012; Fernández-Delgado et al. 2014) and also found wide application in computer vision, e.g. Viola and Jones (2001), Shotton et al. (2011), Criminisi and Shotton (2013), Dollár et al. (2014), Zhang et al.…”
ilastik: interactive machine learning for (bio)image analysis (2019, background): “This property and, most importantly, its good generalization performance (11) make Random Forest particularly well suited for training by non-experts.”
Optimization of Tree Ensembles (2017, methodology): “In a systematic comparison of 179 diﬀerent prediction methods on a broad set of benchmark data sets, Fern´andez-Delgado et al. (2014) found that random forests achieved best or near-best performance over all of these data sets.”
A comparative study of machine learning classifiers for modeling travel mode choice (2017, background): “Advanced classifiers such as random forests or ensemble learners have not been87 considered in a comparative study, even though it has been shown that these classifiers can produce highly88 accurate results for many applications (e.g. Fernández-Delgado et al., 2014).”