What is he best known for?
The fields of study he is best known for:
- Statistics
- Machine learning
- Artificial intelligence
His primary areas of study are Artificial intelligence, Statistics, Machine learning, Algorithm and Data mining.
Jerome H. Friedman interconnects Covariate and Regression in the investigation of issues within Artificial intelligence.
His work on Regression analysis, Optimal discriminant analysis and Recursive partitioning as part of general Statistics study is frequently connected to Bayes error rate and Bayes' theorem, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
Jerome H. Friedman regularly ties together related areas like Econometrics in his Machine learning studies.
Jerome H. Friedman has researched Algorithm in several fields, including Best bin first and Nearest neighbor search.
The various areas that Jerome H. Friedman examines in his Data mining study include Regularization, Range searching, Set, Elastic net regularization and Applied mathematics.
His most cited work include:
- Classification and regression trees (26156 citations)
- The elements of statistical learning : data mining, inference,and prediction (17720 citations)
- The Elements of Statistical Learning (15816 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Machine learning, Statistics, Mathematical optimization and Algorithm are his primary areas of study.
Jerome H. Friedman focuses mostly in the field of Artificial intelligence, narrowing it down to matters related to Regression and, in some cases, Regression analysis.
His work in Machine learning addresses subjects such as Data mining, which are connected to disciplines such as Feature selection.
His Mathematical optimization research is multidisciplinary, incorporating elements of Smoothing and Lasso.
His Lasso course of study focuses on Coordinate descent and Regularization.
His research integrates issues of Variable, Set and Projection pursuit in his study of Algorithm.
He most often published in these fields:
- Artificial intelligence (28.57%)
- Machine learning (20.57%)
- Statistics (14.29%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (28.57%)
- Machine learning (20.57%)
- Lasso (10.29%)
In recent papers he was focusing on the following fields of study:
Jerome H. Friedman focuses on Artificial intelligence, Machine learning, Lasso, Applied mathematics and Pattern recognition.
His Artificial intelligence research includes themes of Tree, Probability distribution and Regression.
His Machine learning study incorporates themes from Statistical learning and Inference.
His Lasso study integrates concerns from other disciplines, such as Algorithm, Generalization, Set and Asymptotic distribution.
The Generalized linear model research Jerome H. Friedman does as part of his general Applied mathematics study is frequently linked to other disciplines of science, such as R package, therefore creating a link between diverse domains of science.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Linear least squares, Sparse regression, Nonparametric regression and Generalized additive model.
Between 2015 and 2021, his most popular works were:
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2nd Edition (133 citations)
- A study of error variance estimation in Lasso regression (85 citations)
- A New Graph-Based Two-Sample Test for Multivariate and Object Data (53 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Machine learning
- Artificial intelligence
Jerome H. Friedman mainly investigates Lasso, Artificial intelligence, Machine learning, Applied mathematics and Statistical learning.
The Lasso study combines topics in areas such as Regularization, Set, Linear regression and Consistency.
His studies in Artificial intelligence integrate themes in fields like Tree and Regression.
His Machine learning research incorporates elements of Sample and Decision rule.
His research integrates issues of Generalization and Elastic net regularization in his study of Applied mathematics.
His study connects Inference and Statistical learning.
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