Friedman, J. H. (Jerome H.)
Overview
Works:  80 works in 208 publications in 2 languages and 2,385 library holdings 

Genres:  Conference papers and proceedings 
Roles:  Author, Thesis advisor, Editor 
Publication Timeline
.
Most widely held works by
J. H Friedman
The elements of statistical learning : data mining, inference, and prediction : with 200 fullcolor illustrations by
Trevor Hastie(
Book
)
52 editions published between 2001 and 2017 in English and held by 1,421 WorldCat member libraries worldwide
Describes important statistical ideas in machine learning, data mining, and bioinformatics. Covers a broad range, from supervised learning (prediction), to unsupervised learning, including classification trees, neural networks, and support vector machines
52 editions published between 2001 and 2017 in English and held by 1,421 WorldCat member libraries worldwide
Describes important statistical ideas in machine learning, data mining, and bioinformatics. Covers a broad range, from supervised learning (prediction), to unsupervised learning, including classification trees, neural networks, and support vector machines
From statistics to neural networks : theory and pattern recognition applications by
Vladimir S Cherkassky(
Book
)
9 editions published in 1994 in English and held by 157 WorldCat member libraries worldwide
This volume provides a unified approach to the study of predictive learning, i.e., generalization from examples. It contains an uptodate review and indepth treatment of major issues and methods related to predictive learning in statistics, Artificial Neural Networks (ANN), and pattern recognition. Topics range from theoretical modeling and adaptive computational methods to empirical comparisons between statistical and ANN methods, and applications. Most contributions fall into one of the three themes: unified framework for the study of predictive learning in statistics and ANNs; similarities and differences between statistical and ANN methods for nonparametric estimation (learning); and fundamental connections between artificial and biological learning systems
9 editions published in 1994 in English and held by 157 WorldCat member libraries worldwide
This volume provides a unified approach to the study of predictive learning, i.e., generalization from examples. It contains an uptodate review and indepth treatment of major issues and methods related to predictive learning in statistics, Artificial Neural Networks (ANN), and pattern recognition. Topics range from theoretical modeling and adaptive computational methods to empirical comparisons between statistical and ANN methods, and applications. Most contributions fall into one of the three themes: unified framework for the study of predictive learning in statistics and ANNs; similarities and differences between statistical and ANN methods for nonparametric estimation (learning); and fundamental connections between artificial and biological learning systems
Multidimensional additive spline approximation by
J. H Friedman(
Book
)
7 editions published between 1980 and 1982 in English and held by 9 WorldCat member libraries worldwide
7 editions published between 1980 and 1982 in English and held by 9 WorldCat member libraries worldwide
Classification and regression trees by
Leo Breiman(
Book
)
6 editions published between 1984 and 2017 in English and held by 8 WorldCat member libraries worldwide
"The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties."Provided by publisher
6 editions published between 1984 and 2017 in English and held by 8 WorldCat member libraries worldwide
"The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties."Provided by publisher
Tōkeiteki gakushū no kiso : dēta mainingu suiron yosoku(
Book
)
1 edition published in 2014 in Japanese and held by 5 WorldCat member libraries worldwide
1 edition published in 2014 in Japanese and held by 5 WorldCat member libraries worldwide
Two papers on range searching by
Jon Louis Bentley(
Book
)
1 edition published in 1978 in English and held by 4 WorldCat member libraries worldwide
1 edition published in 1978 in English and held by 4 WorldCat member libraries worldwide
SMART user's guide by
J. H Friedman(
Book
)
5 editions published in 1984 in English and held by 4 WorldCat member libraries worldwide
Describes software implementing the SMART algorithm. SMART generalizes the projection pursuit method to classification and multiple response regression
5 editions published in 1984 in English and held by 4 WorldCat member libraries worldwide
Describes software implementing the SMART algorithm. SMART generalizes the projection pursuit method to classification and multiple response regression
Projection Pursuit Methods for Data Analysis by
Project ORION (Stanford University. Department of Statistics)(
Book
)
3 editions published in 1981 in English and held by 3 WorldCat member libraries worldwide
The report describes new procedures for multivariate regression and density estimation. The procedures construct models for regression surfaces and densities based on the information contained in suitably closer lowerdimensional projections of the data. Examples illustrating the methods are presented
3 editions published in 1981 in English and held by 3 WorldCat member libraries worldwide
The report describes new procedures for multivariate regression and density estimation. The procedures construct models for regression surfaces and densities based on the information contained in suitably closer lowerdimensional projections of the data. Examples illustrating the methods are presented
Estimating Optimal Transformations for Multiple Regression and Correlation by
Leo Breiman(
Book
)
4 editions published between 1982 and 1983 in English and held by 3 WorldCat member libraries worldwide
Nonlinear transformation of variables is a commonly used practice in regression problems. Two common goals are stabilization of error variance and asymmetrization/normalization of error distribution. A more comprehensive goal, and the one we adopt, is to find those transformations that produce the best fitting additive model. Knowledge of such transformations aid in the interpretation and understanding of the relationship between the response and predictors
4 editions published between 1982 and 1983 in English and held by 3 WorldCat member libraries worldwide
Nonlinear transformation of variables is a commonly used practice in regression problems. Two common goals are stabilization of error variance and asymmetrization/normalization of error distribution. A more comprehensive goal, and the one we adopt, is to find those transformations that produce the best fitting additive model. Knowledge of such transformations aid in the interpretation and understanding of the relationship between the response and predictors
A nested partitioning procedure for numerical multiple integration by
J. H Friedman(
Book
)
2 editions published in 1979 in English and held by 3 WorldCat member libraries worldwide
An algorithm is presented for adaptively partitioning a multidimensional coordinate space based on optimization of a scalar function of the coordinates. The goal is to construct a set of hyperrectangular regions, such that the variation of function values within each region is small. These regions are then used as the basis for a stratified sampling estimate of the definite integral of the function. (Author)
2 editions published in 1979 in English and held by 3 WorldCat member libraries worldwide
An algorithm is presented for adaptively partitioning a multidimensional coordinate space based on optimization of a scalar function of the coordinates. The goal is to construct a set of hyperrectangular regions, such that the variation of function values within each region is small. These regions are then used as the basis for a stratified sampling estimate of the definite integral of the function. (Author)
A variable span smoother by
J. H Friedman(
Book
)
4 editions published in 1984 in English and held by 3 WorldCat member libraries worldwide
A variable span smoother based on linear fits is described. Local crossvalidation is used to estimate the optimal span as a function of abscissa value. Computationally efficient algorithms making use of updating formulas are presented. (Author)
4 editions published in 1984 in English and held by 3 WorldCat member libraries worldwide
A variable span smoother based on linear fits is described. Local crossvalidation is used to estimate the optimal span as a function of abscissa value. Computationally efficient algorithms making use of updating formulas are presented. (Author)
Additive logistic regression : a statistical view of boosting by
J. H Friedman(
Book
)
3 editions published in 1998 in English and held by 3 WorldCat member libraries worldwide
3 editions published in 1998 in English and held by 3 WorldCat member libraries worldwide
Smoothing of scatterplots by
J. H Friedman(
Book
)
4 editions published in 1982 in English and held by 3 WorldCat member libraries worldwide
A variable span scatterplot smoother based on local linear fits is described. Local crossvalidation is used to estimate the optimal span as a function of abscissa value. A rejection rule is suggested to make the smoother resistant against outliers. Computationally efficient algorithms making use of updating formulas and corresponding FORTRAN subroutines are presented
4 editions published in 1982 in English and held by 3 WorldCat member libraries worldwide
A variable span scatterplot smoother based on local linear fits is described. Local crossvalidation is used to estimate the optimal span as a function of abscissa value. A rejection rule is suggested to make the smoother resistant against outliers. Computationally efficient algorithms making use of updating formulas and corresponding FORTRAN subroutines are presented
Exploratory projection pursuit by
Stanford University(
Book
)
3 editions published in 1985 in English and held by 2 WorldCat member libraries worldwide
Exploratory projection pursuit is concerned with finding relatively highly revealing lower dimensional projections of high dimensional data. The intent is to discover views of the multivariate data set that exhibit nonlinear effects  clustering, concentrations near nonlinear manifolds  that are not captured by the linear correlation structure. This paper presents a new algorithm for this purpose that has both statistical and computational advantages over previous methods. A connection to density estimation is established. Examples are presented and issues related to practical application are discussed. Keywords: Exploratory data analysis. (Author)
3 editions published in 1985 in English and held by 2 WorldCat member libraries worldwide
Exploratory projection pursuit is concerned with finding relatively highly revealing lower dimensional projections of high dimensional data. The intent is to discover views of the multivariate data set that exhibit nonlinear effects  clustering, concentrations near nonlinear manifolds  that are not captured by the linear correlation structure. This paper presents a new algorithm for this purpose that has both statistical and computational advantages over previous methods. A connection to density estimation is established. Examples are presented and issues related to practical application are discussed. Keywords: Exploratory data analysis. (Author)
Projection Pursuit Density Estimation by
Project ORION (Stanford University. Department of Statistics)(
Book
)
3 editions published in 1981 in English and held by 2 WorldCat member libraries worldwide
The projection pursuit methodology is applied to the multivariate density estimation problem. The resulting nonparametric procedure is often less biased than kernel and near neighbor methods and does not require the specification of a metric on the data measurement space. In addition, graphical information is produced that can be used to help gain geometric insight into the multivariate data distribution. (Author)
3 editions published in 1981 in English and held by 2 WorldCat member libraries worldwide
The projection pursuit methodology is applied to the multivariate density estimation problem. The resulting nonparametric procedure is often less biased than kernel and near neighbor methods and does not require the specification of a metric on the data measurement space. In addition, graphical information is produced that can be used to help gain geometric insight into the multivariate data distribution. (Author)
The inout method for linear regression with censored data by
J. H Friedman(
Book
)
3 editions published in 1981 in English and held by 2 WorldCat member libraries worldwide
3 editions published in 1981 in English and held by 2 WorldCat member libraries worldwide
Multivariate adaptive regression splines by
J. H Friedman(
Book
)
3 editions published in 1990 in English and held by 2 WorldCat member libraries worldwide
3 editions published in 1990 in English and held by 2 WorldCat member libraries worldwide
Separating Signal From Background Using Ensembles of Rules(
)
1 edition published in 2006 in English and held by 0 WorldCat member libraries worldwide
Machine learning has emerged as a important tool for separating signal events from associated background in high energy particle physics experiments. This paper describes a new machine learning method based on ensembles of rules. Each rule consists of a conjuction of a small number of simple statements (''cuts'') concerning the values of individual input variables. These rule ensembles produce predictive accuracy comparable to the best methods. However their principal advantage lies in interpretation. Because of its simple form, each rule is easy to understand, as is its influence on the predictive model. Similarly, the degree of relevance of each of the respective input variables can be assessed. Graphical representations are presented that can be used to ascertain the dependence of the model jointly on the variables used for prediction
1 edition published in 2006 in English and held by 0 WorldCat member libraries worldwide
Machine learning has emerged as a important tool for separating signal events from associated background in high energy particle physics experiments. This paper describes a new machine learning method based on ensembles of rules. Each rule consists of a conjuction of a small number of simple statements (''cuts'') concerning the values of individual input variables. These rule ensembles produce predictive accuracy comparable to the best methods. However their principal advantage lies in interpretation. Because of its simple form, each rule is easy to understand, as is its influence on the predictive model. Similarly, the degree of relevance of each of the respective input variables can be assessed. Graphical representations are presented that can be used to ascertain the dependence of the model jointly on the variables used for prediction
Data analysis in astronomy II by
V Di Gesù(
)
1 edition published in 1986 in English and held by 0 WorldCat member libraries worldwide
The II international workshop on "Data Analysis in Astronomy" was intended to provide an overview on the state of the art and the trend in data analy sis and image processing in the context of their applications in Astronomy. The need for the organization of a second workshop in this subject derived from the steady. growing and development in the field and from the increasing crossinteraction between methods, technology and applications in Astronomy. The book is organized in four main sections:  Data Analysis Methodologies  Data Handling and Systems dedicated to Large Experiments  Parallel Processing  New Developments The topics which have been selected cover some of the main fields in data analysis in Astronomy. Methods that provide a major contribution to the physical interpretation of the data have been considered. Attention has been devoted to the description of the data analysis and handling organization in very large experiments. A review of the current major satellite and ground based experiments has been included. At the end of the book the following 'Panel Discussions' are included:  Data Analysis Trend in Optical and Radio Astronomy  Data Analysis Trend in X and Gamma Astronomy  Problems and Solutions in the Design of Very Large Experiments  Trend on Parallel Processing Algorithms These contributions in a sense summarize the 'live' reaction of the audience to the various topics
1 edition published in 1986 in English and held by 0 WorldCat member libraries worldwide
The II international workshop on "Data Analysis in Astronomy" was intended to provide an overview on the state of the art and the trend in data analy sis and image processing in the context of their applications in Astronomy. The need for the organization of a second workshop in this subject derived from the steady. growing and development in the field and from the increasing crossinteraction between methods, technology and applications in Astronomy. The book is organized in four main sections:  Data Analysis Methodologies  Data Handling and Systems dedicated to Large Experiments  Parallel Processing  New Developments The topics which have been selected cover some of the main fields in data analysis in Astronomy. Methods that provide a major contribution to the physical interpretation of the data have been considered. Attention has been devoted to the description of the data analysis and handling organization in very large experiments. A review of the current major satellite and ground based experiments has been included. At the end of the book the following 'Panel Discussions' are included:  Data Analysis Trend in Optical and Radio Astronomy  Data Analysis Trend in X and Gamma Astronomy  Problems and Solutions in the Design of Very Large Experiments  Trend on Parallel Processing Algorithms These contributions in a sense summarize the 'live' reaction of the audience to the various topics
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Related Identities
 Tibshirani, Robert Thesis advisor
 Hastie, Trevor Author
 Cherkassky, Vladimir S. Author Editor
 Wechsler, Harry 1948 Editor
 North Atlantic Treaty Organization Scientific Affairs Division
 Gesù, V. Di Author
 Crane, P.
 Scarsi, L.
 Levialdi, S.
 Stuetzle, Werner
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Associated Subjects
Algorithms Approximation theory Artificial intelligence AstronomyData processing Bioinformatics BiologyData processing Computational biology Computational intelligence Computer science Computer vision Database management Data mining Data structures (Computer science) Discriminant analysis Distribution (Probability theory) Electronic data processing Forecasting Inference Information storage and retrieval systems Linear models (Statistics) Machine learning Mathematical statistics Mathematical statisticsComputer programs MathematicsData processing Neural networks (Computer science) Numerical analysis Optical pattern recognition Pattern recognition systems Probabilities Regression analysis Scattering (Mathematics) Spline theory Statistics StatisticsComputer programs StatisticsMethodology Supervised learning (Machine learning) Trees (Graph theory)