Who Cited It

Greedy function approximation: A gradient boosting machine.

2001 · The Annals of Statistics · 30,137 citations · 19 from inside this corpus

Jerome H. Friedman

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

Greedy function approximation: A gradient boosting machine. (2001)Greedy function approximation…The Nature of Statistical Learning Theory (1995)The Nature of Statistical Lea…Learning representations by back-propagating errors (1986)Learning representations by b…Classification and Regression Trees. (1986)Classification and Regression…Generalized Additive Models. (1991)Generalized Additive Models.Multivariate Adaptive Regression Splines (1991)Multivariate Adaptive Regress…Experiments with a new boosting algorithm (1996)Experiments with a new boosti…Pattern Recognition and Neural Networks (1996)Pattern Recognition and Neura…Improved boosting algorithms using confidence-rated predictions (1998)Improved boosting algorithms …XGBoost (2016)XGBoostLightGBM: A Highly Efficient Gradient Boosting Decision Tree (2017)LightGBM: A Highly Efficient …Gradient boosting machines, a tutorial (2013)Gradient boosting machines, a…Machine Learning in Medicine (2015)Machine Learning in MedicineEnsemble learning: A survey (2018)Ensemble learning: A surveyExplainable AI: A Review of Machine Learning Interpretability Methods (2020)Explainable AI: A Review of M…A comparative analysis of gradient boosting algorithms (2020)A comparative analysis of gra…Learning from class-imbalanced data: Review of methods and applications (2016)Learning from class-imbalance…Ensemble deep learning: A review (2022)Ensemble deep learning: A rev…The Boosting Approach to Machine Learning: An Overview (2003)The Boosting Approach to Mach…Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence (2023)Interpreting Black-Box Models…Multi-class AdaBoost (2009)Multi-class AdaBoostPeeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual C… (2014)Peeking Inside the Black Box:…Machine Learning Interpretability: A Survey on Methods and Metrics (2019)Machine Learning Interpretabi…CatBoost for big data: an interdisciplinary review (2020)CatBoost for big data: an int…Over-the-Air Deep Learning Based Radio Signal Classification (2018)Over-the-Air Deep Learning Ba…Reconciling modern machine-learning practice and the classical bias–variance trade-off (2019)Reconciling modern machine-le…Automated Machine Learning (2019)Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models (2020)Visualizing the Effects of Pr…
27 of 27 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.
this paper works it cites works citing it node size = global citations · hover for the full title

What this paper cites, inside the corpus

PaperYearCited
The Nature of Statistical Learning Theory199539,409
Learning representations by back-propagating errors198631,688
Classification and Regression Trees.198621,044
Generalized Additive Models.19918,296
Multivariate Adaptive Regression Splines19918,114
Experiments with a new boosting algorithm19967,582
Pattern Recognition and Neural Networks19966,514
Improved boosting algorithms using confidence-rated predictions19982,573

What cites it, inside the corpus

PaperYearCited
XGBoost201652,304
LightGBM: A Highly Efficient Gradient Boosting Decision Tree20179,490
Gradient boosting machines, a tutorial20133,896
Machine Learning in Medicine20153,534
Ensemble learning: A survey20183,088
Explainable AI: A Review of Machine Learning Interpretability Methods20202,907
A comparative analysis of gradient boosting algorithms20202,692
Learning from class-imbalanced data: Review of methods and applications20162,407
Ensemble deep learning: A review20222,136
The Boosting Approach to Machine Learning: An Overview20032,023
Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence20231,981
Multi-class AdaBoost20091,916
Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual C…20141,838
Machine Learning Interpretability: A Survey on Methods and Metrics20191,822
CatBoost for big data: an interdisciplinary review20201,776
Over-the-Air Deep Learning Based Radio Signal Classification20181,604
Reconciling modern machine-learning practice and the classical bias–variance trade-off20191,561
Automated Machine Learning20191,451
Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models20201,450

Links

DOI · Open access copy · OpenAlex record

Topics

Neural Networks and ApplicationsComputer Science
Machine Learning and AlgorithmsComputer Science
Model Reduction and Neural NetworksPhysics and Astronomy

Is this record sound?

complete

Nothing in this record contradicts itself and no field we check is missing.

  • supports1 author record(s) attached.
  • supports28 reference(s) recorded.
  • neutralThe DOI carries no year to check against.
  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:40+00:00.

sha256 7e3d99a592f7f61f…