Leo Breiman

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A deterministic algorithm for global optimization

Leo Breiman, Adele Cutler, L. Breiman, 1993, Math. Program..

Random Forests

Leo Breiman, L. Breiman, 2001, Machine Learning.

Topics in the Analysis and Optimization of Complex Systems.

William S. Meisel, Leo Breiman, 1977 .

Classification and Regression Trees

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

Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)

Leo Breiman, L. Breiman, 2001 .

Classification of EEG Spatial Patterns with a Tree-Structured Methodology: CART

Walter J. Freeman, Leo Breiman, Kamil A. Grajski, 1986, IEEE Transactions on Biomedical Engineering.

Bias, Variance , And Arcing Classifiers

Leo Breiman, L. Breiman, 1996 .

Pasting Small Votes for Classification in Large Databases and On-Line

Leo Breiman, L. Breiman, 1999, Machine Learning.

2. Mathematical Framework

Leo Breiman, L. Breiman, 1992 .

Bagging Predictors

Leo Breiman, L. Breiman, 1996, Machine Learning.

Bagging Predictors

Leo Breiman, L. Breiman, 1996, Machine Learning.

Random Forests: Finding Quasars

Leo Breiman, John A. Rice, L. Breiman, 2003 .

[The ∏ Method for Estimating Multivariate Functions from Noisy Data]: Response

Leo Breiman, L. Breiman, 1991 .

Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)

Leo Breiman, L. Breiman, 2001, Statistical Science.

The 1991 Census Adjustment: Undercount or Bad Data?

Leo Breiman, 1994 .

Prediction Games and Arcing Algorithms

Leo Breiman, L. Breiman, 1999, Neural Computation.

DISCRETE SIGNALING AND CODING SYSTEMS.

Leo Breiman, 1965 .

Technical note: Some properties of splitting criteria

Leo Breiman, L. Breiman, 2004, Machine Learning.

Two-Eyed Algorithms and Problems

Leo Breiman, L. Breiman, 2003, PKDD.

Using Iterated Bagging to Debias Regressions

Leo Breiman, L. Breiman, 2001, Machine Learning.

Optimal Localization by Pointing Off Axis

Margaret L. Brandeau, Richard A. Olshen, Charles J. Stone, 2010 .

Two-Eyed Algorithms and Problems

Leo Breiman, 2003, PKDD.

Synthetic Aperture Radar Signals: Formulations and Approaches for Data Analysis

Peter Swerling, Leo Breiman, Antonio B Lucero, 1975 .

CART: Classification and Regression Trees

Richard A. Olshen, Charles J. Stone, Leo Breiman, 1984 .

Randomizing Outputs to Increase Prediction Accuracy

Leo Breiman, L. Breiman, 2000, Machine Learning.

8. Convergence in Distribution and the Tools Thereof

Leo Breiman, L. Breiman, 1992 .

Probability and stochastic processes : with a view toward applications

Leo Breiman, L. Breiman, 1970 .

Big Random Forests: Classification and Regression Forests forLarge Data Sets

Leo Breiman, Adele Cutler, Aloysius Lim, 2014 .

Bayes Rules and Partitions

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

Hinging hyperplanes for regression, classification, and function approximation

Leo Breiman, L. Breiman, 1993, IEEE Trans. Inf. Theory.

Stacked regressions

Leo Breiman, L. Breiman, 2004, Machine Learning.

HALF&HALF BAGGING AND HARD BOUNDARY POINTS

Leo Breiman, L. Breiman, 1998 .

DISTRIBUTION BASED TREES ARE MORE ACCURATE

Leo Breiman, Nong Shang, 2013 .

Introduction To Tree Classification

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

Neural Networks and Related Methods for Classification - Discussion

Trevor Hastie, Robert Tibshirani, David J. Hand, 1994 .

Significance Tests.@@@Statistics: With a View Toward Applications.

Joseph B. Kadane, Leo Breiman, Evelyn Caulcott, 1973 .

Tree-Structured Classification Via Generalized Discriminant Analysis: Comment

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

Fitting additive models to regression data

Leo Breiman, L. Breiman, 1993, Computational Statistics & Data Analysis.

Comparing Automatic Smoothers (A Public Service Enterprise)

Steve C. A. Peters, Leo Breiman, L. Breiman, 1992 .

Using convex pseudo-data to increase prediction accuracy

Leo Breiman, 1998 .

Multivariate adaptive regression splines. Discussions

Trevor Hastie, Chong Gu, Grace Wahba, 1991 .

12. Stochastic Processes and Brownian Motion

Leo Breiman, L. Breiman, 1992 .

A Delicate Law of the Iterated Logarithm for Non-Decreasing Stable Processes

Leo Breiman, L. Breiman, 1968 .

On a Simple Estimation Procedure for Censored Regression Models with Known Error Distributions

Leo Breiman, Yacov Tsur, Amos Zemel, 1993 .

Bivariate variable selection for classification problem

Leo Breiman, L. Breiman, Vivian Ng, 2005 .

Time scales, fluctuations and constant flow periods in uni-directional traffic☆

Leo Breiman, R. L. Lawrence, Robert L. Lawrence, 1973 .

Robust confidence bounds for extreme upper quantiles

Leo Breiman, Charles Kooperberg, Charles J. Stone, 1990 .

General Estimates of the Intrinsic Variability of Data in Nonlinear Regression Models

W. S. Meisel, Leo Breiman, L. Breiman, 1976 .

Discussion: Multivariate Adaptive Regression Splines

Leo Breiman, L. Breiman, 1991 .

On the Completeness of Order Statistics

Leo Breiman, C. B. Bell, David Blackwell, 1960 .

On achieving channel capacity in finite-memory channels

Leo Breiman, L. Breiman, 1960 .

BORN AGAIN TREES

Leo Breiman, Nong Shang, L. Breiman, 1996 .

Space-time relationships in one-way traffic flow

Leo Breiman, 1969 .

New Methods for Estimating Tail Probabilities and Extreme Value Disributions.

Leo Breiman, Charles J. Stone, John D Gins, 1979 .

Probability and Stochastic Processes (With a View toward Applications)

D. V. Lindley, Leo Breiman, Leo Breiman, 1970, The Mathematical Gazette.

[Neural Networks: A Review from Statistical Perspective]: Comment

Leo Breiman, L. Breiman, 1994 .

Convergence Properties of a Learning Algorithm

Leo Breiman, Zivia S. Wurtele, 1964 .

1 RANDOM FORESTS

Leo Breiman, 1999 .