Next: IV. Selected Applications
Up: csahtml
Previous: 16.3 Boosting
-
- 1
- Allwein, E., Schapire, R., Singer, Y.: Reducing
multiclass to binary: a unifying approach for margin
classifiers. J. Machine Learning Research 1, 113-141
(2001).
- 2
- Amit, Y., Geman, D.: Shape quantization and
recognition with randomized trees. Neural Computation 9,
1545-1588 (1997).
- 3
- Audrino F., Barone-Adesi G.: A multivariate FGD
technique to improve VaR computation in equity markets. To appear in
Computational Management Science.
- 4
- Audrino, F., Bühlmann, P.: Volatility
estimation with functional gradient descent for very
high-dimensional financial time series. J. Computational Finance
6(3), 65-89 (2003).
- 5
- Bartlett, P.L.: Prediction algorithms: complexity,
concentration and convexity. In: Proceedings of the 13th IFAC
Symposium on System Identification, pp. 1507-1517 (2003).
- 6
- Bartlett, P.L., Jordan, M.I., McAuliffe, J.D.:
Convexity, classification, and risk bounds. Technical Report 638,
Dept. of Statistics, Univ. of Calif. (2003). Available from
http://www.stat.berkeley.edu/tech-reports/index.html
- 7
- Bauer, E., Kohavi, R.: An empirical
comparison of voting classification algorithms: bagging, boosting
and variants. Machine Learning, 36, 1545-1588 (1999).
- 8
- Benner, A.: Application of ``aggregated
classifiers'' in survival time studies. In: COMPSTAT 2002 -
Proceedings in Computational Statistics - 15th Symposium held in
Berlin (Eds. Härdle, W. and Rönz, B.), Physika Verlag, Heidelberg
(2002).
- 9
- Breiman, L.: Bagging predictors. Machine
Learning, 24, 123-140 (1996)
- 10
- Breiman, L.: Out-of-bag
estimation. Technical Report (1996). Available from
ftp://ftp.stat.berkeley.edu/pub/users/breiman/
- 11
- Breiman, L.: Arcing classifiers.
Annals of Statistics 26, 801-824 (1998).
- 12
- Breiman, L.: Prediction games & arcing
algorithms. Neural Computation 11, 1493-1517 (1999).
- 13
- Breiman, L.: Random
Forests. Preprint. Available from
http://stat-www.berkeley.edu/users/breiman/rf.html
- 14
- Breiman, L.: Population theory for boosting
ensembles. To appear in Annals of Statistics, 32(1) (2004).
- 15
- Bühlmann, P.: Bagging, subagging and bragging for
improving some prediction algorithms. In: Recent Advances and Trends
in Nonparametric Statistics (Eds. Akritas, M.G., Politis, D.N.),
Elsevier (2003).
- 16
- Bühlmann, P.: Boosting for high-dimensional
linear models. Preprint (2004). Available from
http://www.stat.math.ethz.ch/~buhlmann/bibliog.html
- 17
- Bühlmann, P., Yu, B: Discussion on Additive
logistic regression: a statistical view of boosting
(Auths. Friedman,J., Hastie, T., Tibshirani,R.) Annals of Statistics
28, 377-386 (2000).
- 18
- Bühlmann, P., Yu, B.: Analyzing
bagging. Annals of Statistics 30, 927-961 (2002).
- 19
- Bühlmann, P., Yu, B.: Boosting with the
loss: regression and classification. J. American Statistical
Association 98, 324-339 (2003).
- 20
- Buja, A., Stuetzle, W.: Observations on
bagging. Preprint (2002). Available from
http://ljsavage.wharton.upenn.edu/buja/
- 21
- Bylander, T.:Estimating generalization error on
two-class datasets using out-of-bag estimates. Machine Learning
48, 287-297 (2002).
- 22
- Chen, S.S., Donoho, D.L., Saunders, M.A.: Atomic
decomposition by basis pursuit. SIAM J. Scientific Computing
20(1), 33-61 (1999).
- 23
- Chen, S.X., Hall, P.: Effects of
bagging and bias correction on estimators defined by estimating
equations. Statistica Sinica 13, 97-109
(2003).
- 24
- Dettling, M.: Bag-Boosting for tumor
classification. In preparation (2004).
- 25
- Dettling, M., Bühlmann, P. Boosting for tumor
classification with gene expression data. Bioinformatics
19(9), 1061-1069 (2003).
- 26
- Borra, S., Di Ciaccio, A.: Improving nonparametric
regression methods by bagging and boosting. Computational Statistics
& Data Analysis 38, 407-420 (2002).
- 27
- Dudoit, S., Fridlyand, J.: Bagging
to improve the accuracy of a clustering procedure. Bioinformatics
19(9), 1090-1099 (2003).
- 28
- Efron, B., Tibshirani, R.: The problem of
regions. Annals of Statistics 26, 1687-1718 (1998).
- 29
- Efron, B., Hastie, T., Johnstone, I.,
Tibshirani, R.: Least angle regression. To appear in Annals of
Statistics, 32(2) (2004).
- 30
- Freund, Y. (1995): Boosting a weak learning algorithm
by majority. Information and Computation 121, 256-285 (1995).
- 31
- Freund, Y., Schapire, R.E.: Experiments with
a new boosting algorithm. In Machine Learning: Proc. Thirteenth
International Conference, pp. 148-156. Morgan Kauffman, San
Francisco (1996).
- 32
- Friedman, J.H.: Multivariate adaptive
regression splines. Annals of Statistics
19, 1-141 (with discussion) (1991).
- 33
- Friedman, J.H.: Greedy function approximation: a
gradient boosting machine. Annals of Statistics 29,
1189-1232 (2001).
- 34
- Friedman, J.H., Hastie, T., Tibshirani,
R.: Additive logistic regression: a statistical view of
boosting. Annals of Statistics 28, 337-407 (with
discussion) (2000).
- 35
- Hastie, T.J., Tibshirani, R.J.: Generalized
Additive Models. Chapman & Hall, London (1990).
- 36
- Hastie, T., Tibshirani, R., Friedman, J.: The
Elements of Statistical Learning. Data Mining, Inference and
Prediction. Springer, New York (2001).
- 37
- Hothorn, T., Lausen, B.: Bundling
classifiers by bagging trees. Preprint (2002). Available from
http://www.mathpreprints.com/math/Preprint/blausen/ 20021016/1/
- 38
- Hurvich, C.M., Simonoff, J.S., Tsai, C.-L.:
Smoothing parameter selection in nonparametric
regression using an improved Akaike information criterion. J. Royal
Statistical Society, Series B, 60, 271-293 (1998).
- 39
- Jiang, W.: process consistency for AdaBoost. To
appear in Annals of Statistics, 32(1) (2004).
- 40
- Lugosi, G., Vayatis, N. On the Bayes-risk
consistency of regularized boosting methods. To appear in Annals of
Statistics, 32(1) (2004).
- 41
- Mallat, S., Zhang, Z. (1993). Matching pursuits with
time-frequency dictionaries. IEEE Transactions Signal Processing
41, 3397-3415 (1993).
- 42
- Mannor, S., Meir, R., Zhang, T.: The consistency of
greedy algorithms for classification. Proceedings COLT02, Vol. 2375
of LNAI, pp. 319-333, Sydney, Springer (2002).
- 43
- Mason, L., Baxter, J. Bartlett, P., Frean,
M.: Functional gradient techniques for combining
hypotheses. In: advances in Large Margin Classifiers
(Eds. Smola, A.J., Bartlett, P.J., Schölkopf, B., Schuurmans, D.). MIT
Press, Cambridge, MA (2000).
- 44
- Politis, D.N., Romano, J.P., Wolf, M.:
Subsampling. Springer, New York (1999).
- 45
- Ridgeway, G.: Looking for lumps: boosting and
bagging for density estimation. Computational Statistics and
Data Analysis 38(4), 379-392 (2002).
- 46
- Rosset, S., Zhu, J., Hastie, T. Margin maximizing loss
functions. Accepted poster for NIPS (2003). Available from
http://www-stat.stanford.edu/hastie/pub.htm
- 47
- Schapire, R.E.: The strength of weak
learnability. Machine Learning 5, 197-227 (1990).
- 48
- Schapire, R.E.: The boosting approach to machine
learning: an overview. In: MSRI Workshop on Nonlinear Estimation and
Classification (Eds. Denison, D.D., Hansen, M.H., Holmes, C.C.,
Mallick, B., Yu, B). Springer, New York (2002).
- 49
- Schölkopf, B., Smola, A.J.: Learning with
Kernels. MIT Press, Cambridge (2002).
- 50
- Tibshirani, R.: Regression shrinkage and selection
via the lasso. J. Royal Statistical Society, Series B, 58,
267-288 (1996).
- 51
- Tukey, J.W.: Exploratory data analysis.
Addison-Wesley, Reading, MA (1977).
- 52
- Tutz, G., Hechenbichler, K.: Aggregating
Classifiers With Ordinal Response Structure, SFB 386 Discussion
Paper No. 359 (2003). Available from
http://www.stat.uni-muenchen.de/sfb386/
- 53
- Vapnik, V.N.: Statistical Learning Theory. Wiley,
New York (1998).
- 54
- Wahba, G.: Spline Models for Observational
Data. Society for Industrial and Applied Mathematics (1990).
- 55
- Zhang, T., Yu, B.: Boosting with early stopping:
convergence and consistency. Technical Report 635, Dept. of
Statistics, Univ. of Calif., Berkeley (2003). Available from
http://www.stat.berkeley.edu/users/binyu/publications.html
- 56
- Zhu, J., Rosset, S., Hastie, T., Tibshirani,
R.: 1-norm support vector machines. Accepted spotlight poster for
NIPS (2003). Available from
http://www-stat.stanford.edu/hastie/pub.htm