Friedman, J. H. (Jerome H.)Overview
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Most widely held works by
J. H Friedman
The elements of statistical learning data mining, inference, and prediction
by Trevor Hastie
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53 editions published between 2001 and 2013 in English and held by 1,154 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
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9 editions published between 2001 and 2004 in English and held by 583 WorldCat member libraries worldwide
From statistics to neural networks : theory and pattern recognition applications
by Vladimir S Cherkassky
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8 editions published in 1994 in English and held by 167 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
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Book
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5 editions published between 1980 and 1982 in English and held by 8 WorldCat member libraries worldwide
Two papers on range searching
by Jon Louis Bentley
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1 edition published in 1978 in English and held by 4 WorldCat member libraries worldwide
Classification and regression trees
by Leo Breiman
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2 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
Estimating Optimal Transformations for Multiple Regression and Correlation
by Leo Breiman
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2 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
Additive logistic regression : a statistical view of boosting
by J. H Friedman
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2 editions published in 1998 in English and held by 3 WorldCat member libraries worldwide
An adaptive importance sampling procedure
by J. H Friedman
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2 editions published in 1981 in English and held by 3 WorldCat member libraries worldwide Monte Carlo calculations often require generation of a random sample of ndimensional points drawn from a specified multivariate probability distribution. We present an importance sampling technique that can often greatly improve the efficiency of an acceptance/rejection generating method. The importance sampling function is defined as piecewise constant on a set of subregions, which are obtained by adaptively partitioning the sampling region so that the variation of density values within each subregion is relatively small. The partitioning strategy is based on multiparameter optimization to estimate the maximum and minimum of the original density function in each subregion. (Author)
Fast MARS
by J. H Friedman
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2 editions published in 1993 in English and held by 2 WorldCat member libraries worldwide
A survey of algorithms and data structures
by Jon Louis Bentley
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3 editions published in 1978 in English and Undetermined and held by 2 WorldCat member libraries worldwide
Projection Pursuit Methods for Data Analysis
by Joseph H Friedman
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1 edition published in 1981 in English and held by 2 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 variable metric decision rule for nonparametric classification
by J. H Friedman
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Book
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1 edition published in 1975 in English and held by 2 WorldCat member libraries worldwide
A dictionary of synonyms and antonyms with 5,000 words most often mispronounced
by Joseph Devlin
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1 edition published in 1987 in English and held by 2 WorldCat member libraries worldwide Lists thousands of words and their synonyms and antonyms; also includes pronunciation for 5000 commonly mispronounced words, and information about roots, prefixes, and suffixes
An algorithm for finding best matches in logarithmic time
by J. H Friedman
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Book
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2 editions published in 1975 in Undetermined and English and held by 2 WorldCat member libraries worldwide
Smoothing of scatterplots
by Joseph H Friedman
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Book
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2 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
Fast algorithms for constructing minimal spanning trees in coordinate spaces
by Jon Louis Bentley
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Book
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1 edition published in 1975 in English and held by 2 WorldCat member libraries worldwide
Flexible metric nearest neighbor classification
by J. H Friedman
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2 editions published in 1994 in English and held by 2 WorldCat member libraries worldwide
Ensemble learning for prediction
by Bogdan E Popescu
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1 edition published in 2004 in English and held by 1 WorldCat member library worldwide The goal of this dissertation is to study and develop automatic prediction technology that is accurate, fast and interpretable. The focus here is on decision tree ensembles methods and extensions. Characteristics of popular ensemble methods such as bagging, random forests and boosting are examined and leveraged to create new predictive methodology. The classic ensembles are integrated in an unifying paradigm, the Important Sampled Learning Ensembles. This framework explains some of the properties of these ensembles and suggests modifications that can significantly enhance their accuracy while dramatically improving their computational performance. The ISLES are twostage algorithms having at the frontend a base learners ensemble generation routine followed by postprocessing algorithms that perform a fast gradient directed regularized fit for regression, robust regression and classification. The postprocessing algorithms developed here can also serve as a standalone toolkit for fitting large linear systems. Decision tree ensembles can generate rules that are fit together with the gradient directed regularized linear algorithms, leading to accurate and interpretable RuleFit models. ISLE and RuleFit are flexible methodologies, able to automatically handle nonlinearities and interactions, mixtures of categorical and continuous variables with missing data, as well as feature selection
Topics in twosample testing
by Nelson C Ray
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1 edition published in 2013 in English and held by 1 WorldCat member library worldwide Driven by recent advances in the collection of biological data, many such studies draw from heterogeneous datasources. We develop an idea of Jerome Friedman's to conduct twosample testing using supervised learning procedures. In special cases, this technique generalizes the randomization ttest, for which an asymptotic normality result is known. Using Stein's method of exchangeable pairs, we produce BerryEsseentype bounds for the permutation tstatistic for the purpose of statistical inference. We demonstrate the use of kernel methods in twosample testing on nonvectorial data (text and images), and apply multiple kernel learning (MKL) to the heterogeneous data domain. We show that these techniques can effectively synthesize signals from multiple datasources and produce interpretable weights that highlight the role of each component more
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Algorithms Approximation theory Artificial intelligence Bioinformatics BiologyData processing Computational biology Computational intelligence Computer science Computer vision Database management Data mining Data structures (Computer science) Decision support systems Decision trees Discriminant analysis Distribution (Probability theory) Electronic data processing English languagePronunciation English languageSynonyms and antonyms 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 Monte Carlo method Nearest neighbor analysis (Statistics) Network analysis (Planning)Data processing Neural networks (Computer science) Numerical analysis Operations research Optical pattern recognition Partitions (Mathematics) Pattern recognition systems Predictive control Regression analysis Sampling (Statistics) Scattering (Mathematics) Search theory Spline theory Statistical decision Statistics 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
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