Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables

By J. Blackard, Denis J. Dean. 1999

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Citations in the corpus

Synthesizing Tabular Data using Generative Adversarial Networks (2018, background): “(3) The Covertype dataset, [5] which predicts the forest cover type.”
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets (2016, methodology): “For a more realistic scenario, we optimized the same SVM hyperparameters without a grid constraint on MNIST and two other prominent UCI datasets (vehicle registration [29] and forest cover types [30]) with more than 50000 data points.”
Deep Weighted Averaging Classifiers (2018, methodology): “ments we make use of datasets of three different types (tabular, image, and text), including both binary and multiclass problems. For tabular data, we use the familiar Adult Income [20] and Covertype [4] datasets available from the UCI machine learning repository, as well as the Lending Club loan da”
DeltaGrad: Rapid retraining of machine learning models (2020, methodology): “We used four datasets for evaluation: MNIST (LeCun et al., 1998), covtype (Blackard & Dean, 1999), HIGGS (Baldi et al., 2014) and RCV1 (Lewis et al., 2004) .”
Making AI Forget You: Data Deletion in Machine Learning (2019, methodology): “[12] J.”
Sub-sampled Newton Methods with Non-uniform Sampling (2016, unknown): “DATASET CT slices[9] Forest[2] Adult[13] Buzz[11] n 53,500 581,012 32,561 59,535 d 385 55 123 78 κ 368 221 182 37 κ̂ 47,078 322,370 69,359 384,580”
Fast Bayesian hyperparameter optimization on large datasets (2017, methodology): “, 2014)), vehicle registration (Siebert, 1987) and forest cover types (Blackard and Dean, 1999) with more than 50000 data points.”
Data-Dependent Convergence for Consensus Stochastic Optimization (2016, methodology): “Covertype is the forest covertype dataset [24] used in [4] obtained from the UC Irvine Machine Learning Repository [25], and rcv1 is from the Reuters collection [26] obtained from libsvm collection [27].”
Asynchronous Gradient Push (2018, unknown): “To facilitate comparisons with existing methods in the literature, a regularized multinomial logistic regression classifier is trained on the covertype dataset [41] from the UCI repository [42].”
Big Batch SGD: Automated Inference using Adaptive Batch Sizes (2016, methodology): “Figure 1 presents the results of our convex experiments on three standard real world datasets: IJCNN1 (Prokhorov, 2001) and COVERTYPE (Blackard and Dean, 1999) for logistic regression, and MILLIONSONG (Bertin-Mahieux et al.”
The Eﬀects of Data Quality on Machine Learning Performance (2022, methodology): “The Covertype dataset was created by the Colorado State University and consists of descriptive information about forested areas and thereby helps natural resource managers in their decision-making processes [7, 8].”
Learning Binary Trees via Sparse Relaxation (2020, methodology): “We experiment on the following datasets: Glass (Dua & Graﬀ, 2017): glass identiﬁcation for forensics, and Covtype (Blackard & Dean, 1999; Dua & Graﬀ, 2017): cartographic variables for forest cover type identiﬁcation.”