Jerome H. Friedman

The Elements of Statistical Learning

Regularization Paths for Generalized Linear Models via Coordinate Descent

We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multi- nom...

Multivariate Adaptive Regression Splines

A new method is presented for flexible regression modeling of high dimensional data. The model takes the form of an expansion in product spline basis functions, where the number...

Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors)

Boosting is one of the most important recent developments in\nclassification methodology. Boosting works by sequentially applying a\nclassification algorithm to reweighted versi...

A Variable Span Smoother

Abstract : A variable span smoother based on linear fits is described. Local cross-validation is used to estimate the optimal span as a function of abscissa value. Computational...

Stochastic gradient boosting

Greedy function approximation: A gradient boosting machine.

Function estimation/approximation is viewed from the perspective\nof numerical optimization in function space, rather than parameter space. A\nconnection is made between stagewi...

Classification and Regression Trees.

A Statistical View of Some Chemometrics Regression Tools

Chemometrics is a field of chemistry that studies the application of statistical methods to chemical data analysis. In addition to borrowing many techniques from the statistics ...