Friedman, J. H. (Jerome H.)
Overview
Works:  93 works in 189 publications in 1 language and 2,057 library holdings 

Genres:  Conference papers and proceedings 
Roles:  Author, Thesis advisor, Editor 
Classifications:  Q325.75, 006.31 
Publication Timeline
.
Most widely held works by
J. H Friedman
The elements of statistical learning : data mining, inference, and prediction by
Trevor Hastie(
Book
)
34 editions published between 2001 and 2013 in English and held by 666 WorldCat member libraries worldwide
"During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics."Jacket
34 editions published between 2001 and 2013 in English and held by 666 WorldCat member libraries worldwide
"During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics."Jacket
The elements of statistical learning : data mining, inference, and prediction : with 200 fullcolor illustrations by
Trevor Hastie(
Book
)
7 editions published between 2001 and 2004 in English and held by 573 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
7 editions published between 2001 and 2004 in English and held by 573 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
)
10 editions published in 1994 in English and held by 159 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
10 editions published in 1994 in English and held by 159 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
)
5 editions published between 1980 and 1982 in English and held by 7 WorldCat member libraries worldwide
5 editions published between 1980 and 1982 in English and held by 7 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
This note describes software implementing the SMART(Smooth Multiple Additive Regression Technique) algorithm. SMART generalizes the projection pursuit method to classification and multiple response regression. SMART also provides a more efficient algorithm for single response projection pursuit regression. Originatorsupplied keywords include: Multiple response regression, Non parametric regression, Classification, and Discriminant analysis
5 editions published in 1984 in English and held by 4 WorldCat member libraries worldwide
This note describes software implementing the SMART(Smooth Multiple Additive Regression Technique) algorithm. SMART generalizes the projection pursuit method to classification and multiple response regression. SMART also provides a more efficient algorithm for single response projection pursuit regression. Originatorsupplied keywords include: Multiple response regression, Non parametric regression, Classification, and Discriminant analysis
Classification and regression trees by
Leo Breiman(
Book
)
3 editions published in 1984 in English and held by 4 WorldCat member libraries worldwide
Introduction to tree classification. Right sized trees and honest estimates. Splitting rules. Strengthening and interpreting. Medical diagnosis and prognosis. Mass spectra classification. regression trees. bayes rules and partitions. Optimal pruning. Construction of trees from a learning sample.Consistency
3 editions published in 1984 in English and held by 4 WorldCat member libraries worldwide
Introduction to tree classification. Right sized trees and honest estimates. Splitting rules. Strengthening and interpreting. Medical diagnosis and prognosis. Mass spectra classification. regression trees. bayes rules and partitions. Optimal pruning. Construction of trees from a learning sample.Consistency
Projection Pursuit Methods for Data Analysis by
J. H Friedman(
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
A survey of algorithms and data structures by
Jon Louis Bentley(
Book
)
4 editions published in 1978 in English and Undetermined and held by 3 WorldCat member libraries worldwide
4 editions published in 1978 in English and Undetermined and held by 3 WorldCat member libraries worldwide
A variable metric decision rule for nonparametric classification by
J. H Friedman(
Book
)
2 editions published in 1975 in English and held by 3 WorldCat member libraries worldwide
2 editions published in 1975 in English and held by 3 WorldCat member libraries worldwide
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
Fast algorithms for constructing minimal spanning trees in coordinate spaces by
Jon Louis Bentley(
Book
)
2 editions published in 1975 in English and held by 3 WorldCat member libraries worldwide
2 editions published in 1975 in English and held by 3 WorldCat member libraries worldwide
An algorithm for finding best matches in logarithmic time by
J. H Friedman(
Book
)
3 editions published in 1975 in English and Undetermined and held by 3 WorldCat member libraries worldwide
3 editions published in 1975 in English and Undetermined and held by 3 WorldCat member libraries worldwide
Projection pursuit regression by
J. H Friedman(
Book
)
3 editions published in 1980 in English and held by 2 WorldCat member libraries worldwide
3 editions published in 1980 in English and held by 2 WorldCat member libraries worldwide
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
M and N plots by
Persi Diaconis(
Book
)
3 editions published in 1980 in English and held by 2 WorldCat member libraries worldwide
3 editions published in 1980 in English and held by 2 WorldCat member libraries worldwide
Smoothing of scatterplots by
Project ORION (Stanford University. Department of Statistics)(
Book
)
4 editions published in 1982 in English and held by 2 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 2 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
Data Analysis in Astronomy by
V Di Ges(
Book
)
2 editions published in 1985 in English and held by 2 WorldCat member libraries worldwide
The international Workshop on "Data Analysis in Astronomy" was in tended to give a presentation of experiences that have been acqui red in data analysis and image processing, developments and appli cations that are steadly growing up in Astronomy. The quality and the quantity of ground and satellite observations require more so phisticated data analysis methods and better computational tools. The Workshop has reviewed the present state of the art, explored new methods and discussed a wide range of applications. The topics which have been selected have covered the main fields of interest for data analysis in Astronomy. The Workshop has been focused on the methods used and their significant applications. Results which gave a major contribution to the physical interpre tation of the data have been stressed in the presentations. Atten tion has been devoted to the description of operational system for data analysis in astronomy. The success of the meeting has been the results of the coordinated effort of several people from the organizers to those who presen ted a contribution and/or took part in the discussion. We wish to thank the members of the Workshop scientific committee Prof. M. Ca paccioli, Prof. G.De Biase, Prof. G.Sedmak, Prof. A.Zichichi and of the local organizing committee Dr.R.Buccheri and Dr. M.C.Macca rone together with Miss P.Savalli and Dr. A.Gabriele of the E.Majo rana Center for their support and the unvaluable part in arranging the Workshop
2 editions published in 1985 in English and held by 2 WorldCat member libraries worldwide
The international Workshop on "Data Analysis in Astronomy" was in tended to give a presentation of experiences that have been acqui red in data analysis and image processing, developments and appli cations that are steadly growing up in Astronomy. The quality and the quantity of ground and satellite observations require more so phisticated data analysis methods and better computational tools. The Workshop has reviewed the present state of the art, explored new methods and discussed a wide range of applications. The topics which have been selected have covered the main fields of interest for data analysis in Astronomy. The Workshop has been focused on the methods used and their significant applications. Results which gave a major contribution to the physical interpre tation of the data have been stressed in the presentations. Atten tion has been devoted to the description of operational system for data analysis in astronomy. The success of the meeting has been the results of the coordinated effort of several people from the organizers to those who presen ted a contribution and/or took part in the discussion. We wish to thank the members of the Workshop scientific committee Prof. M. Ca paccioli, Prof. G.De Biase, Prof. G.Sedmak, Prof. A.Zichichi and of the local organizing committee Dr.R.Buccheri and Dr. M.C.Macca rone together with Miss P.Savalli and Dr. A.Gabriele of the E.Majo rana Center for their support and the unvaluable part in arranging the Workshop
Data analysis in astronomy II by International Workshop on Data Analysis in Astronomy(
)
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
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
more
fewer
Audience Level
0 

1  
Kids  General  Special 
Related Identities
 Hastie, Trevor Author
 Tibshirani, Robert Thesis advisor
 Cherkassky, Vladimir S. Author Editor
 Wechsler, Harry 1948 Editor
 North Atlantic Treaty Organization Scientific Affairs Division
 Scarsi, L.
 Levialdi, S.
 Crane, P.
 Gesù, V. Author
 Gesù, V. Di Author
Useful Links
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) Decision support systems Discriminant analysis Distribution (Probability theory) Electronic data processing File organization (Computer science) Forecasting Inference Information retrieval Information storage and retrieval systems Machine learning Matching theoryData processing Mathematical statistics Mathematical statisticsComputer programs MathematicsData processing Nearest neighbor analysis (Statistics) Network analysis (Planning)Data processing Neural networks (Computer science) Numerical analysis Optical pattern recognition Pattern recognition systems Physics Regression analysis Scattering (Mathematics) Search theory Spline theory Statistical decision Statistics StatisticsComputer programs StatisticsMethodology Supervised learning (Machine learning) Trees (Graph theory)
Alternative Names
Friedman, J.
Friedman, J. H.
Friedman, J. H. 1939
Friedman, Jerome.
Friedman, Jerome 1939
Friedman, Jerome H.
Friedman, Jerome H. 1939...
جروم اچ. فریدمن
Languages
Covers