J. Friedman

发表

Greedy function approximation: A gradient boosting machine.

J. Friedman, 2001 .

PROJECTION PURSUIT DENSITY ESTIMATION

J. Friedman, W. Stuetzle, A. Schroeder, 1984 .

Exploratory Projection Pursuit

J. Friedman, 1987 .

A VARIABLE SPAN SMOOTHER

J. Friedman, 1984 .

Estimating Optimal Transformations for Multiple Regression and Correlation.

J. Friedman, L. Breiman, 1985 .

Special Invited Paper-Additive logistic regression: A statistical view of boosting

J. Friedman, 2000 .

Sparse inverse covariance estimation with the graphical lasso.

R. Tibshirani, T. Hastie, J. Friedman, 2008, Biostatistics.

Estimating Optimal Transformations for Multiple Regression and Correlation: Rejoinder

J. Friedman, L. Breiman, 1985 .

Discussion of the Paper \additive Logistic Regression: a Statistical View of Boosting" By

Y. Freund, R. Schapire, R. Tibshirani, 2000 .

An Algorithm for Finding Best Matches in Logarithmic Expected Time

Jon Louis Bentley, Raphael A. Finkel, Jerome H. Friedman, 1977, TOMS.

Multivariate adaptive regression splines

J. Friedman, 1990 .

A Projection Pursuit Algorithm for Exploratory Data Analysis

John W. Tukey, Jerome H. Friedman, J. Friedman, 1974, IEEE Transactions on Computers.

SparseNet: Coordinate Descent With Nonconvex Penalties

T. Hastie, J. Friedman, R. Mazumder, 2011, Journal of the American Statistical Association.

Predicting Multivariate Responses in Multiple Linear Regression

J. Friedman, L. Breiman, 1997 .

Classification and Regression Trees

Leo Breiman, J. H. Friedman, R. A. Olshen, 1984 .

A Statistical View of Some Chemometrics Regression Tools

J. Friedman, lldiko E. Frank, 1993 .

Multivariate generalizations of the Wald--Wolfowitz and Smirnov two-sample tests

J. Friedman, Lawrence C. Rafsky, 1979 .

A note on the group lasso and a sparse group lasso

R. Tibshirani, T. Hastie, J. Friedman, 2010, 1001.0736.

On Multivariate Goodness-of-Fit and Two-Sample Testing

J. Friedman, 2004 .

Statistical techniques for the classification of chromites in diamond exploration samples

J. Friedman, W. Griffin, N. Fisher, 1997 .

[A Statistical View of Some Chemometrics Regression Tools]: Response

J. Friedman, I. E. Frank, 1993 .

Clustering objects on subsets of attributes (with discussion)

J. Friedman, J. Meulman, 2004 .

Regularized Discriminant Analysis

J. Friedman, 1989 .

FLEXIBLE PARSIMONIOUS SMOOTHING AND ADDITIVE MODELING

J. Friedman, B. Silverman, Bernard Walter Silverman, 1989 .

Strong rules for discarding predictors in lasso‐type problems

R. Tibshirani, T. Hastie, J. Friedman, 2010, Journal of the Royal Statistical Society. Series B, Statistical methodology.

Regularization Paths for Generalized Linear Models via Coordinate Descent.

Trevor Hastie, Jerome Friedman, Rob Tibshirani, 2010, Journal of statistical software.

The Elements of Statistical Learning

Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 2001 .

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition

Robert Tibshirani, Trevor J. Hastie, Jerome H. Friedman, 2001, Springer Series in Statistics.

Additive Logistic Regression : a Statistical View ofBoostingJerome

R. Tibshirani, T. Hastie, J. Friedman, 1998 .

On bagging and nonlinear estimation

J. Friedman, P. Hall, 2007 .

Sparse inverse covariance estimation with the lasso

R. Tibshirani, T. Hastie, J. Friedman, 2007, 0708.3517.

An introduction to multivariate adaptive regression splines

Charles B. Roosen, J. Friedman, 1995, Statistical methods in medical research.

Data Structures for Range Searching

Jon Louis Bentley, Jerome H. Friedman, J. Friedman, 1979, CSUR.

New Insights and Faster Computations for the Graphical Lasso

J. Friedman, D. Witten, N. Simon, 2011 .

Stochastic gradient boosting

J. Friedman, 2002 .

Multidimensional Additive Spline Approximation

J. Friedman, W. Stuetzle, E. Grosse, 1983 .

A Recursive Partitioning Decision Rule for Nonparametric Classification

Jerome H. Friedman, J. Friedman, 1977, IEEE Transactions on Computers.

Flexible Metric Nearest Neighbor Classification

Jerome H. Friedman, J. Friedman, 1994 .

Prototype Methods and Nearest-Neighbors

Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 2009 .

Boosting and Additive Trees

Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 2009 .

Model Assessment and Selection

Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 2009 .

PREDICTIVE LEARNING VIA RULE ENSEMBLES

Bogdan E. Popescu, J. Friedman, 2008, 0811.1679.

Importance Sampled Learning Ensembles

Bogdan E. Popescu, J. Friedman, 2003 .

Clustering objects on subsets of attributes

J. Friedman, 2002 .

The II P method for estimating multivariate functions from noisy data

R. Tibshirani, T. Hastie, J. Friedman, 1991 .

A tree-structured approach to nonparametric multiple regression

J. Friedman, 1979 .

Gradient Directed Regularization

Bogdan E. Popescu, J. Friedman, 2004 .

Regularization paths and coordinate descent

Robert Tibshirani, Trevor J. Hastie, Jerome H. Friedman, 2008, KDD.

Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent.

Trevor Hastie, Noah Simon, Jerome Friedman, 2011, Journal of statistical software.

A Nested Partitioning Procedure for Numerical Multiple Integration

Margaret H. Wright, Jerome H. Friedman, J. Friedman, 1981, TOMS.

Discussion of Boosting Papers

Trevor Hastie, Robert Tibshirani, Ji Zhu, 2003 .

1999 REITZ LECTURE GREEDY FUNCTION APPROXIMATION: A GRADIENT BOOSTING MACHINE'

J. Friedman, 2001 .

An Overview of Predictive Learning and Function Approximation

Jerome H. Friedman, J. Friedman, 1994 .

Estimating Functions of Mixed Ordinal and Categorical Variables Using Adaptive Splines

J. Friedman, 1991 .

An Algorithm for Finding Nearest Neighbors

Forest Baskett, Jerome H. Friedman, Leonard J. Shustek, 1975, IEEE Transactions on Computers.

From Statistics to Neural Networks: Theory and Pattern Recognition Applications

Harry Wechsler, Vladimir Cherkassky, Jerome H. Friedman, 1996 .

Classification and Regression Trees (Wadsworth Statistics/Probability)

J. Friedman, L. Breiman, 1984 .

Approaches to analysis of data that concentrate near higher-dimensional manifolds

J. Friedman, J. Tukey, P. Tukey, 1979 .

Fast Algorithms for Constructing Minimal Spanning Trees in Coordinate Spaces

Jon Louis Bentley, Jerome H. Friedman, J. Friedman, 1978, IEEE Transactions on Computers.

Undirected Graphical Models

Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 2009 .

The Monotone Smoothing of Scatterplots

R. Tibshirani, J. Friedman, 1984 .

The elements of statistical learning. 2001

Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 2001 .

A Study of Error Variance Estimation in Lasso Regression

R. Tibshirani, J. Friedman, Stephen Reid, 2013, 1311.5274.

Applications of a new subspace clustering algorithm (COSA) in medical systems biology

Doris Damian, Matej Orešič, Jerome Friedman, 2007, Metabolomics.

A New Graph-Based Two-Sample Test for Multivariate and Object Data

Jerome H. Friedman, Hao Chen, J. Friedman, 2013, 1307.6294.

ntruders pattern identificatiolll

J. Friedman, I. D. Gesu, Giosue Lo Bosed, 2008 .

Projection Pursuit Regression

J. Friedman, W. Stuetzle, 1981 .

PATHWISE COORDINATE OPTIMIZATION

R. Tibshirani, T. Hastie, J. Friedman, 2007, 0708.1485.

Lasso and Elastic-Net Regularized Generalized Linear Models [R package glmnet version 4.0-2]

R. Tibshirani, T. Hastie, J. Friedman, 2020 .

Graph-Theoretic Measures of Multivariate Association and Prediction

J. Friedman, Lawrence C. Rafsky, 1983 .

Fast sparse regression and classification

J. Friedman, 2012 .

Graphics for the Multivariate Two-Sample Problem

J. Friedman, Lawrence C. Rafsky, 1981 .

Response to Mease and Wyner, Evidence Contrary to the Statistical View of Boosting, JMLR 9:131-156, 2008

Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 2008 .

Rejoinder: Multivariate Adaptive Regression Splines

Jerome H. Friedman, J. Friedman, 1991 .

Tree-Structured Classification Via Generalized Discriminant Analysis: Comment

Leo Breiman, Jerome H. Friedman, J. Friedman, 1988 .

Technology. His Current Research Interests In- Clude Neural Computation, Statistical Learning Theory, and Handwriting Recognition. Kwok and Yeung: Constructive Algorithms for Structure Learning \some Approximation Properties of Projection Pursuit Learning Networks," in Advances in Neural Information

R. S. Schechter, Systems, J. Friedman, 1997 .

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Robert Tibshirani, Trevor J. Hastie, Jerome H. Friedman, 2001, Springer Series in Statistics.

Kernel Smoothing Methods

Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 2009 .

High-Dimensional Problems: p N

Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 2009 .

Discussions of boosting papers, and rejoinders

Trevor Hastie, Peter J. Bickel, Robert Tibshirani, 2004 .

Introduction To Tree Classification

Richard A. Olshen, Charles J. Stone, Leo Breiman, 2017 .

[Flexible Parsimonious Smoothing and Additive Modeling]: Response

J. Friedman, B. Silverman, Bernard Walter Silverman, 1989 .

PRIM-9: An Interactive Multi-dimensional Data Display and Analysis System

John W. Tukey, Jerome H. Friedman, M. A. Fisherkeller, 1975, ACM Pacific.

Representational Gradient Boosting: Backpropagation in the Space of Functions

Efstathios D. Gennatas, J. Friedman, G. Valdes, 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Multiple additive regression trees with application in epidemiology

Jerome H Friedman, Jacqueline J Meulman, J. Friedman, 2003, Statistics in medicine.

Cr-Pyrope Garnets in the Lithospheric Mantle. I. Compositional Systematics and Relations to Tectonic Setting

Jerome H. Friedman, William L. Griffin, Suzanne Y. O'Reilly, 1999 .

Tutorial: Getting Started with MART in R

Jerome H. Friedman, J. Friedman, 2002 .

Lockout: Sparse Regularization of Neural Networks pdf

Yannet Interian, Gilmer Valdes, Jerome H. Friedman, 2021, ArXiv.

Applications of the lasso and grouped lasso to the estimation of sparse graphical models

Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 2010 .

Recent Advances in Predictive (Machine) Learning

Jerome H. Friedman, J. Friedman, 2006, J. Classif..

Bump hunting in high-dimensional data

Nicholas I. Fisher, Jerome H. Friedman, J. Friedman, 1999, Stat. Comput..

Expert-augmented machine learning

Timothy D. Solberg, Eric Eaton, Lyle H. Ungar, 2019, Proceedings of the National Academy of Sciences.

Linear Methods for Regression

Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 2001 .

A NONPARAMETRIC PROCEDURE FOR COMPARING MULTIVARIATE POINT SETS

Jerome H. Friedman, J. Friedman, 2007 .

Predicting Regression Probability Distributions with Imperfect Data Through Optimal Transformations pdf

Jerome H. Friedman, J. Friedman, 2020, ArXiv.

An introduction to real-time graphical techniques for analyzing multivariate data

Jerome H. Friedman, John Alan McDonald, Werner Stuetzle, 1987 .

Graphical Methods of Exploratory Data Analysis

Jerome H. Friedman, John Alan McDonald, Werner Stuetzle, 1985 .

rCOSA: A Software Package for Clustering Objects on Subsets of Attributes

Jerome H. Friedman, Jacqueline J. Meulman, Maarten M. Kampert, 2016, Journal of Classification.

Real Time Graphical Techniques for Analyzing Multivariate Data

Jerome H. Friedman, John Alan McDonald, Werner Stuetzle, 1982 .

Observations of overturning in the upper mesosphere and lower thermosphere and the implications for enhanced winds

J. Friedman, C. Gardner, J. Hecht, 2004 .

Lazy Decision Trees

Ron Kohavi, Jerome H. Friedman, Yeogirl Yun, 1996, AAAI/IAAI, Vol. 1.

On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality

Jerome H. Friedman, J. Friedman, 2004, Data Mining and Knowledge Discovery.

DATA MINING AND STATISTICS: WHAT''S THE CONNECTION

Jerome H. Friedman, J. Friedman, 1997 .

Overview of Supervised Learning

Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 2001 .

Classification and Multiple Regression through Projection Pursuit

Jerome H Friedman, J. Friedman, 1985 .

Building more accurate decision trees with the additive tree

Eric Eaton, Gilmer Valdes, Shane T. Jensen, 2019, Proceedings of the National Academy of Sciences.

Classification: Oldtimers and newcomers

Jerome H. Friedman, Ildiko E. Frank, J. Friedman, 1989 .

Adaptive Spline Networks

Jerome H. Friedman, J. Friedman, 1990, NIPS.

Principal component‐guided sparse regression

Robert Tibshirani, Jerome Friedman, R. Tibshirani, 2018, Canadian Journal of Statistics.

Linear Methods for Classification

Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 2001 .

Contrast trees and distribution boosting

Jerome H. Friedman, J. Friedman, 2019, Proceedings of the National Academy of Sciences.

A Blockwise Descent Algorithm for Group-penalized Multiresponse and Multinomial Regression

Trevor Hastie, Noah Simon, Jerome Friedman, 2013, 1311.6529.

John W. Tukey's work on interactive graphics

Jerome H. Friedman, Werner Stuetzle, J. Friedman, 2002 .

Projection Pursuit Methods for Data Analysis.

Jerome H. Friedman, Werner Stuetzle, J. Friedman, 1982 .

From Statistics to Neural Networks

Harry Wechsler, Vladimir Cherkassky, Jerome H. Friedman, 1994, NATO ASI Series.

Support Vector Machines and Flexible Discriminants

Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 2009 .

Smoothing of Scatterplots

Jerome H. Friedman, Werner Stuetzle, J. Friedman, 1982 .

Additive Models, Trees, and Related Methods

Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 2009 .

Basis Expansions and Regularization

Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 2001 .

Classification and Regression Trees

J. Friedman, R. Olshen, L. Breiman, 1984 .

SMART User's Guide

Jerome H. Friedman, J. Friedman, 1984 .

A Pliable Lasso

Robert Tibshirani, Jerome Friedman, R. Tibshirani, 2017, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America.

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

R. Tibshirani, T. Hastie, J. Friedman, 2004 .

Wavelet-based gradient boosting

J. Friedman, M. Wand, J. Ormerod, 2016, Stat. Comput..

The elements of statistical learning: data mining, inference, and prediction, 2nd Edition

R. Tibshirani, T. Hastie, J. Friedman, 2020 .

Classification and Regression Trees

J. Friedman, R. Olshen, L. Breiman, 1984 .

Construction of Trees from a Learning Sample

J. Friedman, R. Olshen, L. Breiman, 2017 .

An interactive multidimensional data display and analysis system

John W. Tukey, Jerome H. Friedman, M. A. Fisherkeller, 1974 .

Comment: Classifier Technology and the Illusion of Progress

J. Friedman, 2006, math/0606447.

REITZ LECTURE GREEDY FUNCTION APPROXIMATION: A GRADIENT BOOSTING MACHINE 1

J. Friedman, 2022 .

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition

R. Tibshirani, T. Hastie, J. Friedman, 2009, Springer Series in Statistics.