An empirical evaluation of supervised learning in high dimensions

By R. Caruana, Nikolaos Karampatziakis, Ainur Yessenalina. 2008

593 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Evaluation & Benchmarks

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Citations in the corpus

Joint Concept Learning and Semantic Parsing from Natural Language Explanations (2017, background): “Concept learning from labeled examples has been a dominant focus of research in supervised learning (Caruana et al., 2008).”
Meta-Learning With Differentiable Convex Optimization (2019, background): “, [4, 16]) in the low-data regime as they can exploit the negative examples which are often more abundant to learn better class boundaries.”
Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning (2019, background): “Empirical benchmarks have long contributed to the scientific understanding, advancement, and validation of machine learning techniques [8, 6, 7, 46, 14, 13].”
Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning (2012, background): “Recent years have seen an explosion of forest-based techniques in the machine learning, vision and medical imaging literature [9, 15, 25, 31, 35, 37, 58, 59, 65, 67, 68, 69, 74, 79, 89, 92, 97, 110].”
Deep learning in medical imaging and radiation therapy. (2018, background): “In the late 1990s until early 2000s, ANNs started to lose popularity in favor of SVMs and decision-tree-based methods such as random forests and gradient boosting trees that seemed to be more consistently outperforming other learning methods.(22) The reason for this was that ANNs were found to be bo”
A Hierarchy of Limitations in Machine Learning (2020, methodology): “” Caruana et al. (2015) write, “Because the models in this paper are intelligible, it is tempting to interpret them causally.”
Deep Neural Decision Forests (2015, background): “Their performance has been empirically demonstrated to outperform most state-of-the-art learners when it comes to handling high dimensional data problems [6], they are inherently able to deal with multiclass problems, are easily distributable on parallel hardware architectures while being considered”
Approximate Nearest Neighbor Search on High Dimensional Data - Experiments, Analyses, and Improvement (v1.0) (2016, unknown): “For example, there is no evaluation among Rank Cover Tree [23] (from Machine Learning), Product Quantization [27, 20] (from Multimedia), SRS [39] (from Databases), and KGraph [15] (from practitioners).”
Decision Forests for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning (2011, background): “A thorough comparison of forests with respect to other binary classification algorithms has been presented in [15].”
Data mining for credit card fraud: A comparative study (2011, background): “Other studies comparing the performance of different learning algorithms over multiple datasets have found random forest to show good overall performance [9,10,27,31].”
PLANET: Massively Parallel Learning of Tree Ensembles with MapReduce (2009, methodology): “Recent studies have shown that tree models, when combined with ensemble techniques, provide excellent predictive performance across a wide variety of domains [8, 9].”
Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics (2011, result): “This empirical finding is consistent with recent comparative experiments [53], [54] that indicate that Random Forests are robust and perform better than SVM’s for a variety of learning tasks.”
Alternating Decision Forests (2013, unknown): “Although the method is relatively simple, it has many characteristics that make it particularly interesting for computer vision problems: (i) fast training and evaluation, (ii) robustness to label noise, (iii) inherent multi-class capability, (iv) suitability for parallel processing, and (v) good pe”
Machine learning prediction in cardiovascular diseases: a meta-analysis (2020, result): “Both boosting and SVM algorithms have been widely used in biomedicine and prior studies showed mixed results 26–30 .”
Random forests for verbal autopsy analysis: multisite validation study using clinical diagnostic gold standards (2011, background): “The RF has been used extensively in many domains for classification tasks, and is consistently one of the top approaches [5].”
Using methods from the data mining and machine learning literature for disease classification and prediction: A case study examining classification of heart failure sub-types (2013, unknown): “[38] examined the effect of dimensionality (i.”