Peter Bühlmann, Bin Yu  Home/Search
Context
Related
| berkeley.edu/techreports/605.ps.Z Cached: PS.gz PS PDF This document uses CoBlitz to cache paper downloads. If your firewall is blocking outgoing connections to port 3125, you can use these links to download local copies.
Image Update HelpPS.gz PS PDF From: berkeley.edu/techreports...index (more) (Enter author homepages) |
Rate this article:      (best)Comment on this article |
Abstract: This paper investigates a variant of boosting, L 2 Boost, which is constructed from a functional gradient descent algorithm with the L 2 -loss function. Based on an explicit stagewise re tting expression of L 2 Boost, the case of (symmetric) linear weak learners is studied in detail in both regression and two-class classification. In particular, with the boosting iteration m working as the smoothing or regularization parameter, a new exponential bias-variance trade off is found with the... (Update)
Similar documents (at the sentence level):
27.4%: Boosting with the L_2-Loss: Regression and Classification - Bühlmann, Yu (2002) (Correct)
Active bibliography (related documents): More All
0.5: An Empirical Comparison of Pruning methods for Ensemble.. - Windeatt, Ardeshir (Correct)
0.2: Consistency for L2Boosting and Matching Pursuit with Trees and.. - Bühlmann (2002) (Correct)
0.2: Boosting Methods: Why they can be useful for High-Dimensional Data - Bühlmann (2003) (Correct)
Similar documents based on text: More All
0.3: Discussion - Bühlmann, Yu (2003) (Correct)
0.1: Instant Two-Body Equation in Breit Frame - Devine, Wallace (Correct)
0.1: Boosting with Early Stopping: Convergence and Consistency - Zhang, Yu (2003) (Correct)
BibTeX entry: (Update)
@misc{ buhlmann-boosting,
author = "Peter B{\"u}hlmann and Bin Yu",
title = "Boosting with the$L_2$-Loss: Regression and Classification",
url = "citeseer.ist.psu.edu/444884.html" }
Citations (may not include all citations):500 Experiments with a new boosting algorithm - Freund, Schapire - 1996
470 Spline Models for Observational Data (context) - Wahba - 1990
450 Generalized Additive Models - Hastie, Tibshirani - 1990
301 Neural networks and the bias/variance dilemma (context) - Geman, Bienenstock et al. - 1992
273 The strength of weak learnability - Schapire - 1990
236 Additive logistic regression: a statistical view of boosting - Friedman, Hastie et al. - 2000
180 Boosting a weak learning algorithm by majority - Freund - 1995
176 Exploratory data analysis (context) - Tukey - 1977
134 An experimental comparison of three methods for constructing.. - Dietterich - 1999
63 Functional gradient techniques for combining hypotheses (context) - Mason, Baxter et al. - 1999
61 Greedy function approximation: a gradient boosting machine - Friedman - 2001
40 Boosting the margin: A new explanation for the e ectiveness .. (context) - Schapire, Freund et al. - 1998
23 Process consistency for AdaBoost - Jiang - 2000
21 Arcing classiers (context) - Breiman - 1998
10 Logistic regression (context) - Collins, Schapire et al. - 2000
10 Optimal rates of convergence to Bayes risk in nonparametric .. (context) - Marron - 1983
6 Prediction games & arcing algorithms (context) - Breiman - 1999
2 Natural spline functions (context) - Utreras - 1983
2 What happens when bootstrapping the smoothing spline (context) - Gu - 1987
2 Some innity theory for predictor ensembles (context) - Breiman - 2000
2 Smooth discriminant analysis (context) - Mammen, Tsybakov - 1999
1 Comment on \Additive logistic regression: a statistical view.. (context) - Buja - 2000
Documents on the same site (http://www.stat.berkeley.edu/tech-reports/index.html): More
Inverse Problems as Statistics - Stark (2000) (Correct)
A lattice path model for the Bessel polynomials - Pitman (1999) (Correct)
The swine flu vaccine and Guillain-Barré syndrome: a.. - Freedman, Stark (1999) (Correct)
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC