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標(biāo)  題: [轉(zhuǎn)載] Boosting Paper reading
發(fā)信站: 南京大學(xué)小百合站 (Sat Dec 21 21:47:20 2002)

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【 原文由 cloud 所發(fā)表 】

分類(lèi)整理了一下,boosting這幾年了的主要工作.限于bbs的限制,
排版不是很精美.大家多多原諒.


Overview

Robert E. Schapire.  The boosting approach to machine learning: An 
overview. In MSRI Workshop on Nonlinear Estimation and Classification, 
2002. Postscript or gzipped postscript. 

See also many talk slides under \\msrcn\root\share\szli\Boosting

Additive Logistic Models:

Friedman, J. H., Hastie, T. and Tibshirani, R. "Additive Logistic 
Regression: a Statistical View of Boosting." (Aug. 1998)

Y.?Freund and R.E. Schapire. Discussion of the paper ``additive logistic
 regression: a statistical view of boosting'' by J.?Friedman, T.
?Hastie and R.?Tibshirani. The Annals of Statistics, 38(2):391-293, 
2000. (PDF)
Peter Buhlmann and Bin Yu (2000a). Discussion. Additive logistic 
regression: a statistical view of boosting, by Friedman, J., Hastie, T.
 and Tibshirani, R. Annals of Statistics invited (to appear). 
M.?Collins, R.E. Schapire, and Y.?Singer. Logistic regression, 
AdaBoost and bregman distances. In Proceedings of the Thirteenth 
Annual Conference on Computational Learning Theory, 2000. (PDF)
J.H. Friedman. Greedy function approximation: A gradient boosting 
machine. Search for the revised version 2001 Technical report, 
Department of Statistics, Stanford University, February 1999. (PDF) 
Multiple Randmized Classifiers and Trees
Amit, Y. and Gilles Blanchard, Multiple randomized classifiers: MRCL, 
(2001). 

Amit, Y. and Geman, D. Shape quantization and recognition with 
randomized trees, Neural Computation (1997).
Amit, Y., Geman D. and Wilder, K., Joint induction of shape features and
 tree classifiers, IEEE PAMI, (1997).

L.?Breiman. Random forests-random features.  Load updated for Version 
3 Technical Report 567, Statistics Department, University of California,
 September 1999. 
T.G. Dietterich. An experimental comparison of three methods for 
constructing ensembles of decision trees: Bagging, boosting, and 
randomization. Machine Learning, 1999. (PDF) 
Theoretical Analysis and Results on Boosting
E.L. Allwein, R.E. Schapire, and Y.?Singer. Reducing multiclass to 
binary: A unifying approach for margin classifiers. Journal of Machine 
Learning Research, 1:113-141, 2000. (PDF) 
R.E. Schapire. Theoretical views of boosting. In Computational 
Learning Theory: Fourth European Conference, EuroCOLT'99, 1999. (PDF) 
R.E. Schapire. Theoretical views of boosting and applications. In 
Tenth International Conference on Algorithmic Learning Theory, 1999. 
(PDF)
Yoav Freund, Yishay Mansour and Robert E. Schapire. Why averaging 
classifiers can protect against overfitting. Preliminary version 
appeared in Proceedings of the Eighth International Workshop on 
Artificial Intelligence and Statistics, 2001. Postscript or gzipped 
postscript of journal submission (9/4/01).
R.E. Schapire, Y.?Freund, P.?Bartlett, and W.?S. Lee. Boosting the 
margin: A new explanation for the effectiveness of voting methods. The 
Annals of Statistics, 26(5):1651-1686, October 1998. (PDF)

S.?Kutin and P.?Niyogi. The interaction of stability and weakness in 
adaboost. Technical Report University of Chicago Department of 
Computer Science Technical Report TR-2001-30, 2001. (PDF)
S.?Mannor, R.?Meir, and S.?Mendelson. On the consistency of boosting 
algorithms. submitted to Advances in Neural Information Processing 14, 
June 2001. (PDF)
W.?Jiang. Does boosting overfit: Views from an exact solution. Technical
 Report 00-04, Department of Statistics, Northwestern University, 
September 2000. 

Weak Learners that Boost
S.?Mannor and R.?Meir. Weak learners and improved convergence rate in 
boosting. In Advances in Neural Information Processing Systems 13: 
Proc.?NIPS'2000, 2001. (PDF) 
N.?Duffy and D.?Helmbold. Potential boosters?. In S.A. Solla, T.K. Leen,
 and K.-R. Müller, editors, Advances in Neural Information Processing 
Systems 12, pages 258-264. MIT Press, 2000. (PDF) 
W.?Jiang. On weak base hypotheses and their implications for boosting 
regression and classification. Technical Report 00-01, Department of 
Statistics, Northwestern University, October 2000. Former title: ``Large
 Time Behavior of Boosting Algorithms for Regression and 
Classification''. 
Dealing with Noise and Outliers

An adaptive version of the boost by majority algorithm (Freund, 
COLT99) 

G.?R?tsch. Robust Boosting via Convex Optimization. PhD thesis, 
University of Potsdam, October 2001. (PDF)

G.?R?tsch and M.K. Warmuth. Marginal boosting. In Proceedings of the 
Annual Conference on Computational Learning Theory, February 2002. in 
press. (PDF)
Wenxin Jiang. Some theoretical aspects of boosting in the presence of 
noisy data. Technical Report 01-01, Department of Statistics, 
Northwestern University, 2001. To appear in Proceedings: The 
Eighteenth International Conference on Machine Learning (ICML-2001), 
June 2001, Morgan Kaufmann. (PDF) 
Boosting for Regression

Friedman, J. H., Hastie, T. and Tibshirani, R. "Additive Logistic 
Regression: a Statistical View of Boosting." (Aug. 1998). See also 
discussions on this paper in Additive Logistic Models above

M.?Collins, R.E. Schapire, and Y.?Singer. Logistic regression, 
AdaBoost and bregman distances. In Proceedings of the Thirteenth 
Annual Conference on Computational Learning Theory, 2000. (PDF)  See 
also discussions on this paper in Additive Logistic Models above
N.?Duffy and D.?Helmbold. Leaveraging for regression. In Proceedings 
of the Thirteenth Annual Conference on Computational Learning Theory, 
2000. (PDF) 
R.?Avnimelech and N.?Intrator. Boosting regression estimators. Neural 
Computation, 11:491-513, 1999. (PDF) 
G.?R?tsch, A.?Demiriz, and K.?Bennett. Sparse regression ensembles in 
infinite and finite hypothesis spaces. NeuroCOLT2 Technical Report 
2000-085, Royal Holloway College, London, September 2000. accepted for 
publication in the Machine Learning journal special issue on ``New 
Methods for Model Selection and Model Combination''. (PDF) 
R.S. Zemel and T.?Pitassi. A gradient-based boosting algorithm for 
regression problems. In NIPS-13: Advances in Neural Information 
Processing Systems, 13, Cambridge, MA, 2001. MIT Press. In Press. 
(PDF) 
G.?R?tsch, A.?Demiriz, and K.?Bennett. Sparse regression ensembles in 
infinite and finite hypothesis spaces. Machine Learning, 48(1-3):
193-221, 2002. Special Issue on New Methods for Model Selection and 
Model Combination. Also NeuroCOLT2 Technical Report NC-TR-2000-085. 
(PDF) 


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