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Jerome H. Friedman

· Stanford University

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Jerome H. Friedman is a registered researcher in their academic field.

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Statistical Methods and Inference, Sparse and Compressive Sensing Techniques, Face and Expression Recognition · 2010 · PubMed

Regularization Paths for Generalized Linear Models via Coordinate Descent.

Jerome H. Friedman, Trevor Hastie, Rob Tibshirani

We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multinomial regression problems while the penalties include ℓ(1) (the lasso), ℓ(2) (ridge regression) and mixtures of the two (the elastic net). The algorithms use cyclical coordinate descent, computed along a regularization path. The methods can handle large problems and can also deal efficiently with sparse features. In comparative timings we find that the new algorithms are considerably faster than competing methods.

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Neural Networks and Applications, Machine Learning and Algorithms, Model Reduction and Neural Networks · 2001 · The Annals of Statistics

Greedy function approximation: A gradient boosting machine.

Jerome H. Friedman

Function estimation/approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions and steepest-descent minimization. A general gradient descent “boosting” paradigm is developed for additive expansions based on any fitting criterion.Specific algorithms are presented for least-squares, least absolute deviation, and Huber-M loss functions for regression, and multiclass logistic likelihood for classification. Special enhancements are derived for the particular case where the individual additive components are regression trees, and tools for interpreting such “TreeBoost” models are presented. Gradient boosting of regression trees produces competitive, highly robust, interpretable procedures for both regression and classification, especially appropriate for mining less than clean data. Connections between this approach and the boosting methods of Freund and Shapire and Friedman, Hastie and Tibshirani are discussed.

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