Jerome H. Friedman

27 papers A* 1B 1Journal 19Unranked 4
YearRankTypeTitle / Venue / Authors
2024 J jnl
Function Trees: Transparent Machine Learning.
CoRR
Jerome H. Friedman
2022 J jnl
Representational Gradient Boosting: Backpropagation in the Space of Functions.
IEEE Trans. Pattern Anal. Mach. Intell.
Gilmer Valdes, Jerome H. Friedman, Fei Jiang, Efstathios D. Gennatas
2021 J jnl
Lockout: Sparse Regularization of Neural Networks.
CoRR
Gilmer Valdes, Wilmer Arbelo, Yannet Interian, Jerome H. Friedman
2020 J jnl
Predicting Regression Probability Distributions with Imperfect Data Through Optimal Transformations.
CoRR
Jerome H. Friedman
2019 J jnl
Contrast Trees and Distribution Boosting.
CoRR
Jerome H. Friedman
2019 J jnl
Expert-Augmented Machine Learning.
CoRR
Efstathios D. Gennatas, Jerome H. Friedman, Lyle H. Ungar, Romain Pirracchio, Eric Eaton, L. Reichman, Yannet Interian, Charles B. Simone II, A. Auerbach, E. Delgado, Mark J. van der Laan, Timothy D. Solberg, Gilmer Valdes
2017 J jnl
rCOSA: A Software Package for Clustering Objects on Subsets of Attributes.
J. Classif.
Maarten M. Kampert, Jacqueline J. Meulman, Jerome H. Friedman
2016 J jnl
Wavelet-based gradient boosting.
Stat. Comput.
Eugene Dubossarsky, Jerome H. Friedman, John T. Ormerod, Matthew P. Wand
2009 — book
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition.
Trevor Hastie, Robert Tibshirani, Jerome H. Friedman
2008 B conf
Intruders pattern identification.
ICPR
Vito Di Gesù, Giosuè Lo Bosco, Jerome H. Friedman
2008 A* conf
Regularization paths and coordinate descent.
KDD
Trevor Hastie, Jerome H. Friedman, Robert Tibshirani
2006 J jnl
Recent Advances in Predictive (Machine) Learning.
J. Classif.
Jerome H. Friedman
2005 — conf
New Similarity Rules for Mining Data.
WIRN/NAIS
Vito Di Gesù, Jerome H. Friedman
2003 J jnl
Note on "Comparison of Model Selection for Regression" by Vladimir Cherkassky and Yunqian Ma.
Neural Comput.
Trevor Hastie, Robert Tibshirani, Jerome H. Friedman
2001 — book
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Trevor Hastie, Jerome H. Friedman, Robert Tibshirani
1999 J jnl
Bump hunting in high-dimensional data.
Stat. Comput.
Jerome H. Friedman, Nicholas I. Fisher
1997 J jnl
On Bias, Variance, 0/1-Loss, and the Curse-of-Dimensionality.
Data Min. Knowl. Discov.
Jerome H. Friedman
1996 — conf
Lazy Decision Trees.
AAAI/IAAI, Vol. 1
Jerome H. Friedman, Ron Kohavi, Yeogirl Yun
1990 — conf
Adaptive Spline Networks.
NIPS
Jerome H. Friedman
1981 J jnl
A Nested Partitioning Procedure for Numerical Multiple Integration.
ACM Trans. Math. Softw.
Jerome H. Friedman, Margaret H. Wright
1979 J jnl
Data Structures for Range Searching.
ACM Comput. Surv.
Jon Louis Bentley, Jerome H. Friedman
1978 J jnl
Fast Algorithms for Constructing Minimal Spanning Trees in Coordinate Spaces.
IEEE Trans. Computers
Jon Louis Bentley, Jerome H. Friedman
1977 J jnl
A Recursive Partitioning Decision Rule for Nonparametric Classification.
IEEE Trans. Computers
Jerome H. Friedman
1977 J jnl
An Algorithm for Finding Best Matches in Logarithmic Expected Time.
ACM Trans. Math. Softw.
Jerome H. Friedman, Jon Louis Bentley, Raphael A. Finkel
1975 J jnl
An Algorithm for Finding Nearest Neighbors.
IEEE Trans. Computers
Jerome H. Friedman, Forest Baskett, Leonard J. Shustek
1975 — conf
PRIM-9: An Interactive Multi-dimensional Data Display and Analysis System.
ACM Pacific
Mary Anne Fisherkeller, Jerome H. Friedman, John W. Tukey
1974 J jnl
A Projection Pursuit Algorithm for Exploratory Data Analysis.
IEEE Trans. Computers
Jerome H. Friedman, John W. Tukey